Systems and Methods for Encrypting and Controlling Access to Encrypted Data Based Upon Immutable Ledgers

ABSTRACT

Systems and methods for automated blockchain-based recommendation generation, advertising and promotion in accordance with various embodiments of the invention are described. A user device in accordance with an embodiment of the invention includes: a network interface; memory; and a processor. In addition, the processor is configured to implement an execution environment that enables: initiation of transactions via an immutable ledger; recordation of events; updating a user profile, where the user profile comprises at least one characterization associated with the user profile; encrypting the updated user profile and securely storing the encrypted user profile; receiving a request to access the encrypted user profile from a process; determining access permissions of the process; and when the process has sufficient access permissions, decrypting the user profile and providing user profile data to the process.

CROSS-REFERENCE TO RELATED APPLICATIONS

The current application claims the benefit of and priority under 35U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/210,040entitled “Content Recommendation Method” filed Jun. 13, 2021, U.S.Provisional Patent Application No. 63/233,304 entitled “TargetedExecution Environment” filed Aug. 15, 2021, U.S. Provisional PatentApplication No. 63/279,146 entitled “Usage Statistics Tokens andApplications” filed Nov. 14, 2021, and U.S. Provisional PatentApplication No. 63/282,211 entitled “Automated Blockchain BasedRecommendation Generation, Advertising and Promotion” filed Nov. 23,2021, the disclosures of which are hereby incorporated by reference intheir entireties for all purposes.

FIELD OF THE INVENTION

The present invention relates generally to computer security and morespecifically to systems and methods for securely accessing encrypteduser profile data automatically accumulated using entries in immutableledgers.

BACKGROUND

Evaluation of product and service suitability for a person ororganization is becoming increasingly complex. Historically, manyproducts have been mass-produced, and evaluated using the reputation ofthe manufacturer or the brick-and-mortar stores that sell the products.Among other things, this fails in a world where more and more productsare sold online, by sellers that are not recognized by the buyers.Custom products, such as artwork, have been evaluated by a small groupof knowledgeable specialists (e.g., artists, investors, or resellers)and the larger public have merely accepted their wisdom and preferencesas valuable. This becomes untenable when the perceived number ofproducts dramatically increases, at which time only the most recognizedproducts and brands (such as individual paintings and artists) will berecognized, and a large body of lesser-known quality products will beundervalued by the greater masses. It may also be said that the highervolume, most widely adopted products, generally lack in quality.Similarly, services can maintain a reputation when there are relativelyfew services and service providers, and it is easy to select amainstream choice; however, as services increasingly become customized,the evaluation of quality becomes more difficult. Digital goods andservices are no different, and there are many examples that show howrecommendation systems fail, are gamed, or do not represent the likelypreferences of an individual whose opinions are not aligned with otherconsumers. Moreover, in an area where speculation is rife and valuefluctuates rapidly, and sometimes due to manipulation of themarketplace, whether by users or bots, there is very little trust inrecommendations as the risks of abuse or other failures are high. Thedirest situation arises for online sales of customized products andservices from largely unknown but potentially very talented providers,which is the direction the Internet is currently headed in terms ofcommerce.

SUMMARY OF THE INVENTION

Systems and methods for automated blockchain-based recommendationgeneration, advertising and promotion in accordance with variousembodiments of the invention are described.

A recommendation platform in accordance with an embodiment of theinvention includes: a network interface; memory; and a processor, theprocessor configured to:

retrieve transaction data records stored within an immutable ledger,where each retrieved data record is associated with a public key; and

each public key is associated with a user profile;

identify a need associated with a specific user profile based uponinformation including retrieved data records associated with the publickey of the specific user profile;

identify retrieved data records containing information relevant to theidentified need;

identify user profiles associated with the retrieved data recordscontaining information relevant to the identified need;

determine weights based upon similarity of the identified user profilesassociated with the retrieved data records containing informationrelevant to the identified need and the specific user profile;

weight the information from the retrieved data records containinginformation relevant to the identified need based upon the determinedweights;

generate a recommendation with respect to a resource based upon theweighted information from the retrieved data records containinginformation relevant to the identified need; and

transmit the recommendation to a user device associated with thespecific user profile.

In a further embodiment, the resource is at least one of a specificproduct and a specific service.

In another embodiment, the retrieved transaction data records storedwithin an immutable ledger comprise transactions reflecting actionsassociated with user profiles.

In a still further embodiment, the processor is further configured to:determine a precision of the recommendation based upon the weightedinformation from the retrieved data records containing informationrelevant to the identified need; and transmit the precision to the userdevice associated with the specific user profile.

In still another embodiment, the processor is further configured todetermine that the precision is insufficient to generate arecommendation.

In a yet further embodiment, the determination that the precision isinsufficient to generate a recommendation comprises comparing thedetermined precision to a threshold.

In yet another embodiment, the recommendation comprises renderinginformation that directs the user device how to render therecommendation via a user interface.

In a further embodiment again, the recommendation includes a pluralityof alternative recommendations.

In another embodiment again, the processor is further configured todetermine weights by generating a matrix of weights for the specificuser profile.

In a further additional embodiment, the matrix of weights includes: afirst dimension that corresponds to different user profiles; and asecond dimension that corresponds to a weighted array, where each entryin the weighted array corresponds to a weight for one resourcecharacterized in that the weight indicates the similarity of thespecific user profile with one of the different user profiles.

In another additional embodiment, the matrix of weights furthercomprises a third dimension corresponding to different resources.

In a still yet further embodiment, the matrix of weights furthercomprises at least one dimension representing at least one of temporalaspects, and preferences.

In still yet another embodiment, the processor is further configured toestimate the scarcity of a resource to which the recommendation relates.

In a still further embodiment again, the processor is further configuredto receive the specific user profile as an input.

In still another embodiment again, the specific user profile includes atleast one selected from the group consisting of:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

In a still further additional embodiment, the processor is furtherconfigured to identify opinions associated with the specific userprofile.

In still another additional embodiment, at least one of the opinionsassociated with the specific user profile is identified within theretrieved transaction data records.

In a yet further embodiment again, the processor is further configuredto use the specific user profile to retrieve at least one additionalsource of information selected from the group consisting of:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

In yet another embodiment again, the processor is further configured tostore information concerning features of the resource as anN-dimensional feature vector.

In a yet further additional embodiment, the processor is furtherconfigured to generate a self-organized map of a plurality of resourcesbased upon N-dimensional feature vectors for each of the plurality ofresources.

In yet another additional embodiment, the processor is furtherconfigured to generate a lower dimensional representation of a pluralityof resources based upon N-dimensional feature vectors for each of theplurality of resources.

In a further additional embodiment again, the processor is furtherconfigured to identify a subspace of a plurality of resources based uponN-dimensional feature vectors for each of the plurality of resources.

In another additional embodiment again, the subspace corresponds to aplurality of resources having at least one characteristic relevant tothe identified need.

In a still yet further embodiment again, the processor is furtherconfigured to detect user profiles associated with abuse based upon theretrieve transaction data records and a representation of a plurality ofresources.

In still yet another embodiment again, the processor is furtherconfigured to automatically generate user profiles.

In a still yet further additional embodiment, the retrieved transactiondata records include information concerning conversion of a previouslygenerated recommendation and the processor is further configured todetermine an attribution for the conversion to one of the user profilesassociated with the retrieved data records.

A process in accordance with an embodiment of the invention includes:

retrieving data records from at least one immutable ledger using arecommendation platform, where each retrieved data record is associatedwith a public key;

associating information obtained from retrieved data records associatedwith specific public keys using the recommendation platform;

generating at least one user profile based on information associatedwith specific public keys using the recommendation platform;

generating at least one characterization for association within the atleast one user profile based on information associated with specificpublic keys using the recommendation platform;

receiving a request with respect to an identifier associated with anidentified user profile using the recommendation platform; and

generating a response to the request based upon at least onecharacterization associated with the identified user profile using therecommendation platform.

A further embodiment of the process of the invention further includescreating a cluster of user profiles based upon characterizationsassociated with the clustered user profiles.

In another embodiment of the process of the invention, the response is arecommendation generated based upon the cluster of user profiles.

In a still further embodiment of the method of the invention, therequest includes a template identifying at least one characterization.

Still another embodiment of the method of the invention further includesreceiving information associated with the at least one user profile fromat least one end-user module present on at least one user device.

In a yet further embodiment of the method of the invention, theinformation associated with the at least one user profile is a token.

In yet another embodiment of the method of the invention, the responseis a token.

A further embodiment again of the method of the invention furtherincludes receiving information associated with the at least one userprofile from at least one execution environment present on at least oneuser devices.

A user device in accordance with an embodiment of the inventionincludes: a network interface; memory; and a processor, the processorconfigured to implement an execution environment that enables:

initiation of transactions via an immutable ledger;

recordation of events;

updating a user profile, where the user profile comprises at least onecharacterization associated with the user profile;

encrypting the updated user profile and securely storing the encrypteduser profile;

receiving a request to access the encrypted user profile from a process;

determining access permissions of the process; and

when the process has sufficient access permissions, decrypting the userprofile and providing user profile data to the process.

In a further embodiment, the events comprise user request and useractions.

In another embodiment, the processor is further configured to transmituser profile data for remote storage.

In a still further embodiment, the processor is further configured toreceive user profile data from a remote source and the executionenvironment enables updating of the user profile data of the encrypteduser profile based upon the received user profile data.

In still another embodiment, the processor is further configured tocreate an append-only area of user profile data using a public keyassociated with the execution environment and a corresponding privatekey.

In a yet further embodiment, the request includes a pseudonym and theprocess is determined to have sufficient access permissions when thepseudonym is matches a pseudonym associated with the user profile.

A recommendation platform in accordance with another embodiment of theinvention includes: a network interface; memory; and a processor, theprocessor configured to:

retrieve transaction data records stored within an immutable ledger,where each retrieved data record is associated with a public key; and

each public key is associated with a user profile;

identify a plurality of needs;

identify retrieved data records containing information relevant to eachof the plurality of identified needs;

identify user profiles associated with the retrieved data recordscontaining information relevant to the identified need;

determine a plurality of recommendations with respect to each of theplurality of identified needs based upon the retrieved data recordscontaining information relevant to each of the plurality of identifiedneeds;

identify a need from the plurality of identified needs associated with aspecific user profile based upon information including retrieved datarecords associated with the public key of the specific user profile;

determine weights based upon similarity of the identified user profilesassociated with the retrieved data records containing informationrelevant to the identified need from the plurality of identified needsand the specific user profile;

weight the plurality of recommendations determined with respect toidentified need from the plurality of identified needs based upon thedetermined weights;

generate a recommendation based upon the plurality of weightedrecommendations; and

transmit the recommendation to a user device associated with thespecific user profile.

In a further embodiment, the resource is at least one of a specificproduct and a specific service.

In another embodiment, the retrieved transaction data records storedwithin an immutable ledger comprise transactions reflecting actionsassociated with user profiles.

In a still further embodiment, the processor is further configured to:determine a precision of the recommendation based upon the weightedinformation from the retrieved data records containing informationrelevant to the identified need; and transmit the precision to the userdevice associated with the specific user profile.

In still another embodiment, the processor is further configured todetermine that the precision is insufficient to generate arecommendation.

In a yet further embodiment, the determination that the precision isinsufficient to generate a recommendation comprises comparing thedetermined precision to a threshold.

In yet another embodiment, the recommendation comprises renderinginformation that directs the user device how to render therecommendation via a user interface.

In a further embodiment again, the recommendation includes a pluralityof alternative recommendations.

In another embodiment again, the processor is further configured todetermine weights by generating a matrix of weights for the specificuser profile.

In a further additional embodiment, the matrix of weights includes: afirst dimension that corresponds to different user profiles; and asecond dimension that corresponds to a weighted array, where each entryin the weighted array corresponds to a weight for one resourcecharacterized in that the weight indicates the similarity of thespecific user profile with one of the different user profiles.

In another additional embodiment, the matrix of weights furthercomprises a third dimension corresponding to different resources.

In a still yet further embodiment, the matrix of weights furthercomprises at least one dimension representing at least one of temporalaspects, and preferences.

In still yet another embodiment, the processor is further configured toestimate the scarcity of a resource to which the recommendation relates.

In a still further embodiment again, the processor is further configuredto receive the specific user profile as an input.

In still another embodiment again, the specific user profile includes atleast one selected from the group consisting of:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

In a still further additional embodiment, the processor is furtherconfigured to identify opinions associated with the specific userprofile.

In still another additional embodiment, at least one of the opinionsassociated with the specific user profile is identified within theretrieved transaction data records.

In a yet further embodiment again, the processor is further configuredto use the specific user profile to retrieve at least one additionalsource of information selected from the group consisting of:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

In yet another embodiment again, the processor is further configured tostore information concerning features of the resource as anN-dimensional feature vector.

In a yet further additional embodiment, the processor is furtherconfigured to generate a self-organized map of a plurality of resourcesbased upon N-dimensional feature vectors for each of the plurality ofresources.

In yet another additional embodiment, the processor is furtherconfigured to generate a lower dimensional representation of a pluralityof resources based upon N-dimensional feature vectors for each of theplurality of resources.

In a further additional embodiment again, the processor is furtherconfigured to identify a subspace of a plurality of resources based uponN-dimensional feature vectors for each of the plurality of resources.

In another additional embodiment again, the subspace corresponds to aplurality of resources having at least one characteristic relevant tothe identified need.

In a still yet further embodiment again, the processor is furtherconfigured to detect user profiles associated with abuse based upon theretrieve transaction data records and a representation of a plurality ofresources.

In still yet another embodiment again, the processor is furtherconfigured to automatically generate user profiles.

In a still yet further additional embodiment, the retrieved transactiondata records include information concerning conversion of a previouslygenerated recommendation and the processor is further configured todetermine an attribution for the conversion to one of the user profilesassociated with the retrieved data records.

BRIEF DESCRIPTION OF THE DRAWINGS

The description and claims will be more fully understood with referenceto the following figures and data graphs, which are presented asexemplary embodiments of the invention and should not be construed as acomplete recitation of the scope of the invention.

FIG. 1 is a conceptual diagram of an NFT platform in accordance with anembodiment of the invention.

FIG. 2 is a network architecture diagram of an NFT platform inaccordance with an embodiment of the invention.

FIG. 3 is a conceptual diagram of a permissioned blockchain inaccordance with an embodiment of the invention.

FIG. 4 is a conceptual diagram of a permissionless blockchain inaccordance with an embodiment of the invention.

FIGS. 5A-5B are diagrams of a dual blockchain in accordance with anumber of embodiments of the invention.

FIG. 6 conceptually illustrates a process followed by a Proof of Workconsensus mechanism in accordance with an embodiment of the invention.

FIG. 7 conceptually illustrates a process followed by a Proof of Spaceconsensus mechanism in accordance with an embodiment of the invention.

FIG. 8 illustrates a dual proof consensus mechanism configuration inaccordance with an embodiment of the invention.

FIG. 9 illustrates a process followed by a Trusted ExecutionEnvironment-based consensus mechanism in accordance with someembodiments of the invention

FIGS. 10-12 depicts various devices that can be utilized alongside anNFT platform in accordance with various embodiments of the invention.

FIG. 13 depicts a media wallet application configuration in accordancewith an embodiment of the invention.

FIGS. 14A-14C depicts user interfaces of various media walletapplications in accordance with a number of embodiments of theinvention.

FIG. 15 illustrates an NFT ledger entry corresponding to an NFTidentifier.

FIGS. 16A-16B illustrate an NFT arrangement relationship withcorresponding physical content in accordance with an embodiment of theinvention.

FIG. 17 illustrates a process for establishing a relationship between anNFT and corresponding physical content.

FIG. 18 illustrates a process for generating a recommendation using arecommendation platform in accordance with an embodiment of theinvention.

FIG. 19 illustrates a process for generating recommendations based uponweights determined for a user in accordance with an embodiment of theinvention.

FIG. 20 illustrates a process for determining a recommendation basedboth on the likely preferences of one or more users, and based on thelikely price trends for the products or services in accordance with anembodiment of the invention.

FIG. 21 conceptually illustrates a process for generating a user profilein accordance with an embodiment of the invention.

FIG. 22 conceptually illustrates a process for determining weights basedupon a user profile in accordance with an embodiment of the invention.

FIG. 23 conceptually illustrates a process for generating priceestimates in accordance with an embodiment of the invention.

FIG. 24 conceptually illustrates a process for generating abetting-based recommendation in accordance with an embodiment of theinvention.

FIG. 25 conceptually illustrates a prior art cryptographic coin.

FIG. 26 conceptually illustrates a cryptographic token that can be usedto mint smart contracts enabling the buying and selling ofrecommendation opinions in accordance with an embodiment of theinvention.

FIG. 27 conceptually illustrates a process for selecting items inaccordance with an embodiment of the invention using a smart contractsimilar to those described above with reference to FIG. 26 .

FIG. 28 conceptually illustrates a process for generatingrecommendations for an offeror in accordance with an embodiment of theinvention.

FIG. 29 illustrates parameters of a betting-related contract structure.

FIG. 30 illustrates a cryptographic token that can be utilized to mintsmart contracts used to buy and sell recommendations in accordance withanother embodiment of the invention.

FIG. 31 conceptually illustrates two smart contracts that can beutilized for automated trading.

FIG. 32 conceptually illustrates a process for grouping items with theintent of forming a recommendation in accordance with an embodiment ofthe invention.

FIG. 33 conceptually illustrates a space with a collection of 16 itemsand their corresponding characteristics in accordance with an embodimentof the invention.

FIG. 34 conceptually illustrates the collection of items shown in FIG.33 reorganized utilizing a self-organizing map in accordance with anembodiment of the invention.

FIG. 35 illustrates a process for computing suggestions, rankings,recommendations, and similar in accordance with an embodiment of theinvention.

FIG. 36 conceptually illustrates a process for generatingrecommendations based upon opinions expressed by different userentities.

FIG. 37 illustrates a process for identification of potential abuseassociated with bid-based recommendation mechanisms in accordance withan embodiment of the invention.

FIG. 38 conceptually illustrates a process for identification ofexpected or suggested market pricing in accordance with an embodiment ofthe invention.

FIG. 39 conceptually illustrates A process for evaluatingrecommendations based upon information received from bounty hunters inaccordance with an embodiment of the invention.

FIG. 40 illustrates a process for mining data from one or moreblockchains to build and/or update user profiles in accordance with anembodiment of the invention.

FIG. 41 illustrates a process for determining user similarity inaccordance with an embodiment of the invention.

FIG. 42 conceptually illustrates the processing of data by an end-usermodule implemented in accordance with an embodiment of the invention.

FIG. 43 illustrates an example of a template that can be utilized by anend-user module similar to the end-user module described above withreference to FIG. 42 .

FIG. 44 illustrates the selection and distribution of promotionalcontent.

FIG. 45 illustrates a log generated by a service provider as a result ofthe placement of promotional content for a user associated with end-usermodule in accordance with an embodiment of the invention.

FIG. 46 illustrates an execution environment (EE) that observes andprocesses events in accordance with an embodiment of the invention.

FIG. 47 illustrates a process for synchronization of profile storageassociated with an EE in accordance with an embodiment of the invention.

FIG. 48 conceptually illustrates a content token with associated dataimplemented in accordance with an embodiment of the invention.

FIG. 49 conceptually illustrates an EE that implements append-onlyfunctionality in accordance with an embodiment of the invention.

FIG. 50 illustrates a process for determining an attribution associatedwith a conversion in accordance with an embodiment of the invention.

FIG. 51 conceptually illustrates a process for utilizing pseudonyms tosecurely access profile data in accordance with an embodiment of theinvention.

FIG. 52 illustrates an advertiser utilizing a dual execution environmentconfiguration to influence a potential buyer.

DETAILED DESCRIPTION

Turning now to the drawings, systems and methods for automatedblockchain-based recommendation generation, advertising and promotion inaccordance with various embodiments of the invention are illustrated. Inmany embodiments, a blockchain-based Non-Fungible Token (NFT) platformis provided that includes a recommendation platform that is capable ofgenerating recommendations regarding products and/or services.Recommendation platforms in accordance with several embodiments of theinvention can provide recommendations with respect to one or moreresources, such as physical items, services, or digital items, includingNFTs, based upon a variety of factors. In the context ofblockchain-based NFT platforms, recommendations can include (but are notlimited to) advertisements and/or promotions that involve payment on thepart of an advertiser and/or promoter in exchange for the presentationof a particular recommendation. While many of the processes forgenerating recommendations described herein focus on the generation ofrecommendations for the acquisition of resources, recommendation canalso be generated with respect to the provision of resources and/or forthe benefits of offerors in accordance with various embodiments of theinvention. As can readily be appreciated, the specific recommendationsgenerated by a recommendation platform implemented within ablockchain-based NFT platform are largely only limited by therequirements of a given application.

People buy or acquire rights to products and services, whether physicalor virtual, for a variety of reasons. One reason is to address apersonal, family or organizational need related to the acquirer.Examples of this are to rent a movie, buy a phone, buy a ringtone, orpurchase a virtual sword for a game. Here, a product or a service is agood match if it is priced right relative to the expected enjoyment ofit by the acquirer. Another reason is to address the needs of anotherentity, e.g., to acquire a product or service for a client or as ahousewarming gift for an acquaintance. In this case, an acquisition isgood if it matches the needs of another entity, while remaining withinthe budgetary boundaries of the acquirer. A third reason is for purposesof an investment. A good acquisition is one that increases in value,which happens when it is increasingly appreciated by one or more(potentially unknown) tentative later acquirers, whether due to beingseen as a good investment to these, or by matching another reason toacquire for one or more of them. At the same time, investments also comewith ownership benefits before a resale, whether this be the opportunityto enjoy using the product or service, e.g., drive a car, own a lakesideproperty, view an art piece, or due to the prestige that comes withownership.

Current recommendation mechanisms typically fail to address thesecomplexities. Most notably, (a) they do not address the individualaspects, i.e., do not attempt to identify what endorsements are mostlikely to be most relevant to a tentative buyer based on an alignment ofpreferences; and (b) they do not predict likely future trends in value,and therefore do not address the investment angle to any extent at all.

Recommendation platforms in accordance with many embodiments of theinvention identify likely alignment of a product and/or service with oneor more users. In a number of embodiments, the recommendation platformdetermines alignment based upon one or more factors indicative of (butnot limited to) the expected enjoyment and/or the expected utility valueof the product or service. In many embodiments, recommendation platformsobserve actions of users. The observed actions can be used to generateuser profiles, which can then be utilized to generate recommendations.In a number of embodiments, the similarity (or dissimilarity) of userprofiles can be utilized to weight user actions. Recommendationplatforms can then use weighted information to determine the likelihoodthat an action such as (but not limited to) a review is relevant to aneed of a particular user. Furthermore, actions of users with similaruser profiles can be given greater weight in the generation ofrecommendations. In certain embodiments, the recommendation platformprovides additional information regarding the precision and/orconfidence of a recommendation to enable a user to assess the extent towhich the recommendation can be relied upon.

In a number of embodiments, recommendation platforms generaterecommendations by assessing a product's feature vector. In certainembodiments, the features of a product are expressed as an N-dimensionalfeature vector. In many embodiments, the process of generating arecommendation involves an estimate of the scarcity of the particulargood and/or service to which the recommendation relates. In certainembodiments, the recommendation platform also estimates informationregarding users including (but not limited to) demographic information.

In many embodiments, the recommendation platform utilizes betting-basedinformation in the generation of recommendations. In severalembodiments, betting information can be accumulated through the mintingof smart contracts that enable users to provide predictions. In thisway, information concerning potential outcomes can be written to animmutable ledger and then utilized in the formation of recommendations.

In certain embodiments, processes for generating recommendations alsoinclude automatically detecting abuse (e.g., paid reviews and/or otheractions). In many embodiments, processes for abuse detection rely upon auser-specific self-organized map that is based solely on usersthemselves as nodes. In a number of embodiments, bounty hunters assistwith the detection of abuse. As can readily be appreciated, theinformation gathered by recommendation platforms can be utilized in avariety of ways to detect patterns of suspicious behavior and/or userbehavior indicative of abuse.

Rich user profiles can be highly beneficial in the generation ofrelevant recommendations. Recommendation platforms in accordance withmany embodiments of the invention build profiles and generate associatedcharacterizations based upon information including (but not limited to)transaction data recorded in immutable ledgers. In many embodiments,recommendation platforms automatically scan blockchain entries andrecord data associated with specific public keys. In severalembodiments, applications that interact with blockchains can provideadditional information that can be utilized to build and/or maintainuser profiles. In several embodiments, end-user modules in softwareapplications automatically provide user data to recommendationplatforms. In a number of embodiments, the recommendation platformcommunicates with an execution environment that can securely provideuser profile information.

In several embodiments, recommendation platforms can securely share userprofile data with advertisers using templates. In certain embodiments,an end-user module, e.g., in the form of a wallet or a browser plugin,may determine that the user associated with it has one or more interestsassociated with one or more of the templates expressed by a givenadvertiser. In many embodiments, the end-user module can generate atoken that includes information expressing the user's interest alignedwith these templates. This token may be purchased by the advertiser,from the end user module, or obtained in exchange for a service.

Recommendation platforms and processes for automatically building userprofiles and/or generating recommendations in accordance with variousembodiments of the invention are discussed in detail below. Whilerecommendation platforms are not limited to use in blockchain-basednon-fungible (NFT) platforms, blockchain-based non-fungible (NFT)platforms that can include recommendation platforms are introduced belowas an illustrative example of the manner in which recommendationplatforms in accordance with various of the embodiments of the inventioncan be implemented within blockchain-based systems.

Non-Fungible Token (NFT) Platforms

Turning now to the drawings, systems and methods for implementingblockchain-based Non-Fungible Token (NFT) platforms in accordance withvarious embodiments of the invention are illustrated. In severalembodiments, blockchain-based NFT platforms are platforms which enablecontent creators to issue, mint, and transfer Non-Fungible Tokens (NFTs)directed to content including, but not limited to, rich media content.

In a number of embodiments, content creators can issue NFTs to userswithin the NFT platform. NFTs can be created around a large range ofreal-world media content and intellectual property. Movie studios canmint digital collectibles for their movies, characters, notable scenesand/or notable objects. Record labels can mint digital collectibles forartists, bands, albums and/or songs. Similarly, official digital tradingcards can be made from likeness of celebrities, cartoon charactersand/or gaming avatars.

NFTs minted using NFT platforms in accordance with various embodimentsof the invention can have multifunctional programmable use casesincluding rewards, private access to premium content and experiences, asdiscounts toward the purchase of goods, among many other value-added usecases.

In many embodiments, each NFT can have a set of attributes that defineits unique properties. NFTs may therefore be classified based on whichattributes are emphasized. Possible classifications may address, but arenot limited to: NFTs as identifying entities, NFTs output by other NFTs,NFTs as content creation assets, and NFTs as evaluating entities. NFTscan be interpreted differently by various platforms in order to createplatform-specific user experiences. The metadata associated with an NFTmay also include digital media assets such as (but not limited to)images, videos about the specific NFT, and the context in which it wascreated (studio, film, band, company song etc.).

In many embodiments, NFT storage may be facilitated through mechanismsfor the transfer of payment from users to one or more service providers.Through these mechanisms, a payment system for NFT maintenance can allowfor incremental payment and ongoing asset protection. NFT storage may beadditionally self-regulated through willing participants disclosingunsatisfactory NFT management in exchange for rewards.

In many embodiments, the NFT platform can include media walletapplications that enable users to securely store NFTs and/or othertokens on their devices. Furthermore, media wallets (also referred to as“digital wallets”) can enable users to obtain NFTs that prove purchaseof rights to access a particular piece of media content on one platformand use the NFT to gain access to the purchased content on anotherplatform. The consumption of such content may be governed by contentclassification directed to visual user interface systems.

In several embodiments, users can download and install media walletapplications to store NFTs on the same computing devices used to consumestreamed and/or downloaded content. Media wallet applications and NFTscan disseminate data concerning media consumption on the computingdevices on which the media wallet applications are installed and/orbased upon observations indicative of media consumption independently ofthe device. Media consumption data may include, but is not limited to,data reporting the occurrence of NFT transactions, data reporting theoccurrence of NFT event interactions data reporting the content of NFTtransactions, data reporting the content of media wallet interactions,and/or data reporting the occurrence of media wallet interactions.

While various aspects of NFT platforms, NFTs, media wallets, blockchainconfigurations, reporting structures, and maintenance systems arediscussed above, NFT platforms and different components that can beutilized within NFT platforms in accordance with various embodiments ofthe invention are discussed further below.

NFT Platforms

An NFT platform in accordance with an embodiment of the invention isillustrated in FIG. 1 . The NFT platform 100 utilizes one or moreimmutable ledgers (e.g. one or more blockchains) to enable a number ofverified content creators 104 to access an NFT registry service to mintNFTs 106 in a variety of forms including (but not limited to) celebrityNFTs 122, character NFTs from games 126, NFTs that are redeemable withingames 126, NFTs that contain and/or enable access to collectibles 124,and NFTs that have evolutionary capabilities representative of thechange from one NFT state to another NFT state.

Issuance of NFTs 106 via the NFT platform 100 enables verification ofthe authenticity of NFTs independently of the content creator 104 byconfirming that transactions written to one or more of the immutableledgers are consistent with the smart contracts 108 underlying the NFTs.

As is discussed further below, content creators 104 can provide the NFTs106 to users to reward and/or incentivize engagement with particularpieces of content and/or other user behavior including (but not limitedto) the sharing of user personal information (e.g. contact informationor user ID information on particular services), demographic information,and/or media consumption data with the content creator and/or otherentities. In addition, the smart contracts 108 underlying the NFTs cancause payments of residual royalties 116 when users engage in specifictransactions involving NFTs (e.g. transfer of ownership of the NFT).

In a number of embodiments, users utilize media wallet applications 110on their devices to store NFTs 106 distributed using the NFT platform100. Users can use media wallet applications 110 to obtain and/ortransfer NFTs 106. In facilitating the retention or transfer of NFTs106, media wallet applications may utilize wallet user interfaces thatengage in transactional restrictions through either uniform orpersonalized settings. Media wallet applications 110 in accordance withsome embodiments may incorporate NFT filtering systems to avoidunrequested NFT assignment. Methods for increased wallet privacy mayalso operate through multiple associated wallets with varyingcapabilities. As can readily be appreciated, NFTs 106 that areimplemented using smart contracts 108 having interfaces that comply withopen standards are not limited to being stored within media wallets andcan be stored in any of a variety of wallet applications as appropriateto the requirements of a given application. Furthermore, a number ofembodiments of the invention support movement of NFTs 106 betweendifferent immutable ledgers. Processes for moving NFTs between multipleimmutable ledgers in accordance with various embodiments of theinvention are discussed further below.

In several embodiments, content creators 104 can incentivize users togrant access to media consumption data using offers including (but notlimited to) offers of fungible tokens 118 and/or NFTs 106. In this way,the ability of the content creators to mint NFTs enables consumers toengage directly with the content creators and can be utilized toincentivize users to share with content creators' data concerning userinteractions with additional content. The permissions granted byindividual users may enable the content creators 104 to directly accessdata written to an immutable ledger. In many embodiments, thepermissions granted by individual users enable authorized computingsystems to access data within an immutable ledger and content creators104 can query the authorized computing systems to obtain aggregatedinformation. Numerous other example functions for content creators 104are possible, some of which are discussed below.

NFT blockchains in accordance with various embodiments of the inventionenable issuance of NFTs by verified users. In many embodiments, theverified users can be content creators that are vetted by anadministrator of networks that may be responsible for deploying andmaintaining the NFT blockchain. Once the NFTs are minted, users canobtain and conduct transactions with the NFTs. In several embodiments,the NFTs may be redeemable for items or services in the real world suchas (but not limited to) admission to movie screenings, concerts, and/ormerchandise.

As illustrated in FIG. 1 , users can install the media walletapplication 110 onto their devices and use the media wallet application110 to purchase fungible tokens. In many embodiments, the fungibletokens can be fully converted into fiat currency and/or othercryptocurrency. In several embodiments, the fungible tokens areimplemented using split blockchain models in which the fungible tokenscan be issued to multiple blockchains (e.g. Ethereum). As can readily beappreciated, the fungible tokens and/or NFTs utilized within an NFTplatform in accordance with various embodiments of the invention arelargely dependent upon the requirements of a given application.

In several embodiments, the media wallet application is capable ofaccessing multiple blockchains by deriving accounts from each of thevarious immutable ledgers used within an NFT platform. For each of theseblockchains, the media wallet application can automatically providesimplified views whereby fungible tokens and NFTs across multipleaccounts and/or multiple blockchains can be rendered as single userprofiles and/or wallets. In many embodiments, the single view can beachieved using deep-indexing of the relevant blockchains and APIservices that can rapidly provide information to media walletapplications in response to user interactions. In certain embodiments,the accounts across the multiple blockchains can be derived using BIP32deterministic wallet key. In other embodiments, any of a variety oftechniques can be utilized by the media wallet application to access oneor more immutable ledgers as appropriate to the requirements of a givenapplication.

NFTs can be purchased by way of exchanges 130 and/or from other users.In addition, content creators can directly issue NFTs to the mediawallets of specific users (e.g. by way of push download or AirDrop). Inmany embodiments, the NFTs are digital collectibles such as celebrityNFTs 122, character NFTs from games 126, NFTs that are redeemable withingames 126, and/or NFTs that contain and/or enable access to collectibles124. It should be appreciated that a variety of NFTs are describedthroughout the discussion of the various embodiments described hereinand can be utilized in any NFT platform and/or with any media walletapplication.

While the NFTs are shown as static in the illustrated embodiment,content creators can utilize users' ownership of NFTs to engage inadditional interactions with the user. In this way, the relationshipbetween users and particular pieces of content and/or particular contentcreators can evolve over time around interactions driven by NFTs. In anumber of embodiments, collection of NFTs can be gamified to enableunlocking of additional NFTs. In addition, leaderboards can beestablished with respect to particular content and/or franchises basedupon users' aggregation of NFTs. As is discussed further below, NFTsand/or fungible tokens can also be utilized by content creators toincentivize users to share data.

NFTs minted in accordance with several embodiments of the invention mayincorporate a series of instances of digital content elements in orderto represent the evolution of the digital content over time. Each one ofthese digital elements can have multiple numbered copies, just like alithograph, and each such version can have a serial number associatedwith it, and/or digital signatures authenticating its validity. Thedigital signature can associate the corresponding image to an identity,such as the identity of the artist. The evolution of digital content maycorrespond to the transition from one representation to anotherrepresentation. This evolution may be triggered by the artist, by anevent associated with the owner of the artwork, by an external eventmeasured by platforms associated with the content, and/or by specificcombinations or sequences of event triggers. Some such NFTs may alsohave corresponding series of physical embodiments. These may be physicaland numbered images that are identical to the digital instancesdescribed above. They may also be physical representations of anothertype, e.g., clay figures or statues, whereas the digital representationsmay be drawings. The physical embodiments may further be of differentaspects that relate to the digital series. Evolution in compliance withsome embodiments may also be used to spawn additional content, forexample, one NFT directly creating one or more secondary NFTs.

When the user wishes to purchase an NFT using fungible tokens, mediawallet applications can request authentication of the NFT directly basedupon the public key of the content creator and/or indirectly based upontransaction records within the NFT blockchain. As discussed above,minted NFTs can be signed by content creators and administrators of theNFT blockchain. In addition, users can verify the authenticity ofparticular NFTs without the assistance of entities that minted the NFTby verifying that the transaction records involving the NFT within theNFT blockchain are consistent with the various royalty paymenttransactions required to occur in conjunction with transfer of ownershipof the NFT by the smart contract underlying the NFT.

Applications and methods in accordance with various embodiments of theinvention are not limited to media wallet applications or use within NFTplatforms. Accordingly, it should be appreciated that the datacollection capabilities of any media wallet application described hereincan also be implemented outside the context of an NFT platform and/or ina dedicated application and/or in an application unrelated to thestorage of fungible tokens and/or NFTs. Various systems and methods forimplementing NFT platforms and media wallet applications in accordancewith various embodiments of the invention are discussed further below.

NFT Platform Network Architectures

NFT platforms in accordance with many embodiments of the inventionutilize public blockchains and permissioned blockchains. In severalembodiments, the public blockchain is decentralized and universallyaccessible. Additionally, in a number of embodiments,private/permissioned blockchains are closed systems that are limited topublicly inaccessible transactions. In many embodiments, thepermissioned blockchain can be in the form of distributed ledgers, whilethe blockchain may alternatively be centralized in a single entity.

An example of network architecture that can be utilized to implement anNFT platform including a public blockchain and a permissioned blockchainin accordance with several embodiments of the invention is illustratedin FIG. 2 . The NFT platform 200 utilizes computer systems implementinga public blockchain 202 such as (but not limited to) Ethereum andSolana. A benefit of supporting interactions with public blockchains 202is that the NFT platform 200 can support minting of standards based NFTsthat can be utilized in an interchangeable manner with NFTs minted bysources outside of the NFT platform on the public blockchain. In thisway, the NFT platform 200 and the NFTs minted within the NFT platformare not part of a walled garden, but are instead part of a broaderblockchain-based ecosystem. The ability of holders of NFTs minted withinthe NFT platform 200 to transact via the public blockchain 202 increasesthe likelihood that individuals acquiring NFTs will become users of theNFT platform. Initial NFTs minted outside the NFT platform can also bedeveloped through later minted NFTs, with the initial NFTs being used tofurther identify and interact with the user based upon their ownershipof both NFTs. Various systems and methods for facilitating therelationships between NFTs, both outside and within the NFT platform arediscussed further below.

Users can utilize user devices configured with appropriate applicationsincluding (but not limited to) media wallet applications to obtain NFTs.In many embodiments, media wallets are smart device enabled, front-endapplications for fans, influencers, trend spotters and/or consumers,central to all user activity on an NFT platform. As is discussed indetail below, different embodiments of media wallet applications canprovide any of a variety of functionality that can be determined asappropriate to the requirements of a given application. In theillustrated embodiment, the user devices 206 are shown as mobile phonesand personal computers. As can readily be appreciated user devices canbe implemented using any class of consumer electronics device including(but not limited to) tablet computers, laptop computers, televisions,game consoles, virtual reality headsets, mixed reality headsets,augmented reality headsets, media extenders, and/or set top boxes asappropriate to the requirements of a given application.

In many embodiments, NFT transaction data entries in the permissionedblockchain 208 are encrypted using users' public keys so that the NFTtransaction data can be accessed by the media wallet application. Inthis way, users control access to entries in the permissioned blockchain208 describing the user's NFT transaction. In several embodiments, userscan authorize content creators 204 to access NFT transaction datarecorded within the permissioned blockchain 208 using one of a number ofappropriate mechanisms including (but not limited to) compoundidentities where the user is the owner of the data and the user canauthorize other entities as guests that can also access the data. As canreadily be appreciated, particular content creators' access to the datacan be revoked by revoking their status as guests within the compoundentity authorized to access the NFT transaction data within thepermissioned blockchain 208. In certain embodiments, compound identitiesare implemented by writing authorized access records to the permissionedblockchain using the user's public key and the public keys of the othermembers of the compound entity.

When content creators wish to access particular pieces of data storedwithin the permissioned blockchain 208, they can make a request to adata access service. The data access service may grant access to datastored using the permissioned blockchain 208 when the content creators'public keys correspond to public keys of guests. In a number ofembodiments, guests may be defined within a compound identity. Theaccess record for the compound entity may also authorize the compoundentity to access the particular piece of data. In this way, the user hascomplete control over access to their data at any time by admitting orrevoking content creators to a compound entity, and/or modifying theaccess policies defined within the permissioned blockchain 208 for thecompound entity. In several embodiments, the permissioned blockchain 208supports access control lists and users can utilize a media walletapplication to modify permissions granted by way of the access controllist. In many embodiments, the manner in which access permissions aredefined enables different restrictions to be placed on particular piecesof information within a particular NFT transaction data record withinthe permissioned blockchain 208. As can readily be appreciated, themanner in which NFT platforms and/or immutable ledgers providefine-grained data access permissions largely depends upon therequirements of a given application.

In many embodiments, storage nodes within the permissioned blockchain208 do not provide content creators with access to entire NFTtransaction histories. Instead, the storage nodes simply provide accessto encrypted records. In several embodiments, the hash of the collectionof records from the permissioned blockchain is broadcast. Therefore, therecord is verifiably immutable and each result includes the hash of therecord and the previous/next hashes. As noted above, the use of compoundidentities and/or access control lists can enable users to grantpermission to decrypt certain pieces of information or individualrecords within the permissioned blockchain. In several embodiments, theaccess to the data is determined by computer systems that implementpermission-based data access services.

In many embodiments, the permissioned blockchain 208 can be implementedusing any blockchain technology appropriate to the requirements of agiven application. As noted above, the information and processesdescribed herein are not limited to data written to permissionedblockchains 208, and NFT transaction data simply provides an example.Systems and methods in accordance with various embodiments of theinvention can be utilized to enable applications to provide fine-grainedpermission to any of a variety of different types of data stored in animmutable ledger as appropriate to the requirements of a givenapplication in accordance with various embodiments of the invention.

While various implementations of NFT platforms are described above withreference to FIG. 2 , NFT platforms can be implemented using any numberof immutable and pseudo-immutable ledgers as appropriate to therequirements of specific applications in accordance with variousembodiments of the invention. Blockchain databases in accordance withvarious embodiments of the invention may be managed autonomously usingpeer-to-peer networks and distributed timestamping servers. In someembodiments, any of a variety of consensus mechanisms may be used bypublic blockchains, including but not limited to Proof of Spacemechanisms, Proof of Work mechanisms, Proof of Stake mechanisms, andhybrid mechanisms.

NFT platforms in accordance with many embodiments of the invention maybenefit from the oversight and increased security of privateblockchains. As can readily be appreciated, a variety of approaches canbe taken to the writing of data to permissioned blockchains and theparticular approach is largely determined by the requirements ofparticular applications. As such, computer systems in accordance withvarious embodiments of the invention can have the capacity to createverified NFT entries written to permissioned blockchains.

An implementation of permissioned (or private) blockchains in accordancewith some embodiments of the invention is illustrated in FIG. 3 .Permissioned blockchains 340 can typically function as closed computingsystems in which each participant is well defined. In severalembodiments, private blockchain networks may require invitations. In anumber of embodiments, entries, or blocks 320, to private blockchainscan be validated. In some embodiments, the validation may come fromcentral authorities 330. Private blockchains can allow an organizationor a consortium of organizations to efficiently exchange information andrecord transactions. Specifically, in a permissioned blockchain, apreapproved central authority 330 (which should be understood aspotentially encompassing multiple distinct authorized authorities) canapprove a change to the blockchain. In a number of embodiments, approvalmay come without the use of a consensus mechanism involving multipleauthorities. As such, through a direct request from users 310 to thecentral authority 330, the determination of whether blocks 320 can beallowed access to the permissioned blockchain 340 can be determined.Blocks 320 needing to be added, eliminated, relocated, and/or preventedfrom access may be controlled through these means. In doing so thecentral authority 330 may manage accessing and controlling the networkblocks incorporated into the permissioned blockchain 340. Upon theapproval 350 of the central authority, the now updated blockchain 360can reflect the added block 320.

NFT platforms in accordance with many embodiments of the invention mayalso benefit from the anonymity and accessibility of a publicblockchain. Therefore, NFT platforms in accordance with many embodimentsof the invention can have the capacity to create verified NFT entrieswritten to a permissioned blockchain.

An implementation of a permissionless, decentralized, or publicblockchain in accordance with an embodiment of the invention isillustrated in FIG. 4 . In a permissionless blockchain, individual users410 can directly participate in relevant networks and operate asblockchain network devices 430. As blockchain network devices 430,parties would have the capacity to participate in changes to theblockchain and participate in transaction verifications (via the miningmechanism). Transactions are broadcast over the computer network anddata quality is maintained by massive database replication andcomputational trust. Despite being decentralized, an updated blockchain460 cannot remove entries, even if anonymously made, making itimmutable. In many decentralized blockchains, many blockchain networkdevices 430, in the decentralized system may have copies of theblockchain, allowing the ability to validate transactions. In manyinstances, the blockchain network device 430 can personally addtransactions, in the form of blocks 420 appended to the publicblockchain 440. To do so, the blockchain network device 430 would takesteps to allow for the transactions to be validated 450 through variousconsensus mechanisms (Proof of Work, proof of stake, etc.). A number ofconsensus mechanisms in accordance with various embodiments of theinvention are discussed further below.

Additionally, in the context of blockchain configurations, the termsmart contract is often used to refer to software programs that run onblockchains. While a standard legal contract outlines the terms of arelationship (usually one enforceable by law), a smart contract enforcesa set of rules using self-executing code within NFT platforms. As such,smart contracts may have the means to automatically enforce specificprogrammatic rules through platforms. Smart contracts are oftendeveloped as high-level programming abstractions that can be compileddown to bytecode. Said bytecode may be deployed to blockchains forexecution by computer systems using any number of mechanisms deployed inconjunction with the blockchain. In many instances, smart contractsexecute by leveraging the code of other smart contracts in a mannersimilar to calling upon a software library.

A number of existing decentralized blockchain technologies intentionallyexclude or prevent rich media assets from existing within theblockchain, because they would need to address content that is notstatic (e.g., images, videos, music files). Therefore, NFT platforms inaccordance with many embodiments of the invention may address this withblockchain mechanisms, that preclude general changes but account forupdated content.

NFT platforms in accordance with many embodiments of the invention cantherefore incorporate decentralized storage pseudo-immutable dualblockchains. In some embodiments, two or more blockchains may beinterconnected such that traditional blockchain consensus algorithmssupport a first blockchain serving as an index to a second, or more,blockchains serving to contain and protect resources, such as the richmedia content associated with NFTs.

In storing rich media using blockchain, several components may beutilized by an entity (“miner”) adding transactions to said blockchain.References, such as URLs, may be stored in the blockchain to identifyassets. Multiple URLs may also be stored when the asset is separatedinto pieces. An alternative or complementary option may be the use ofAPIs to return either the asset or a URL for the asset. In accordancewith many embodiments of the invention, references can be stored byadding a ledger entry incorporating the reference enabling the entry tobe timestamped. In doing so, the URL, which typically accounts fordomain names, can be resolved to IP addresses. However, when only filesof certain types are located on particular resources, or where smallportions of individual assets are stored at different locations, usersmay require methods to locate assets stored on highly-splintereddecentralized storage systems. To do so, systems may identify at leastprimary asset destinations and update those primary asset destinationsas necessary when storage resources change. The mechanisms used toidentify primary asset destinations may take a variety of formsincluding, but not limited to, smart contracts.

A dual blockchain, including decentralized processing 520 anddecentralized storage 530 blockchains, in accordance with someembodiments of the invention is illustrated in FIG. 5A. Applicationrunning on devices 505, may interact with or make a request related toNFTs 510 interacting with such a blockchain. An NFT 510 in accordancewith several embodiments of the invention may include many valuesincluding generalized data 511 (e.g. URLs), and pointers such as pointerA 512, pointer B 513, pointer C 514, and pointer D 515. In accordancewith many embodiments of the invention, the generalized data 511 may beused to access corresponding rich media through the NFT 510. The NFT 510may additionally have associated metadata 516.

Pointers within the NFT 510 may direct an inquiry toward a variety of onor off-ledger resources. In some embodiments of the invention, asillustrated FIG. 5A, pointer A 512 can direct the need for processing tothe decentralized processing network 520. Processing systems areillustrated as CPU A, CPU B, CPU C, and CPU D 525. The CPUs 525 may bepersonal computers, server computers, mobile devices, edge IoT devices,etc. Pointer A may select one or more processors at random to performthe execution of a given smart contract. The code may be secure ornonsecure and the CPU may be a trusted execution environment (TEE),depending upon the needs of the request. In the example reflected inFIG. 5A, pointer B 513, pointer C 514, and pointer D 515 all point to adecentralized storage network 530 including remote off-ledger resourcesincluding storage systems illustrated as Disks A, B, C, and D 535.

The decentralized storage system may co-mingle with the decentralizedprocessing system as the individual storage systems utilize CPUresources and connectivity to perform their function. From a functionalperspective, the two decentralized systems may also be separate. PointerB 513 may point to one or more decentralized storage networks 530 forthe purposes of maintaining an off-chain log file of token activity andrequests. Pointer C 514 may point to executable code within one or moredecentralized storage networks 530. And Pointer D 515 may point torights management data, security keys, and/or configuration data withinone or more decentralized storage networks 530.

An additional benefit of blockchains exists in the possibility ofincorporating methods for detection of abuse, essentially a “bountyhunter” 550. FIG. 5B illustrates the inclusion of bounty hunters 550within dual blockchain structures implemented in accordance with anembodiment of the invention. Bounty hunters 550 allow NFTs 510, whichcan point to networks that may include decentralized processing 520and/or storage networks 530, to be monitored. The bounty hunter's 550objective may be to locate incorrectly listed or missing data andexecutable code within the NFT 510 or associated networks. Additionally,the miner 540 can have the capacity to perform all necessary mintingprocesses or any process within the architecture that involves aconsensus mechanism.

Bounty hunters 550 may also choose to verify each step of a computation,and if they find an error, submit evidence of this in return for somereward. This can have the effect of invalidating the incorrect ledgerentry and, potentially based on policies, all subsequent ledger entries.Such evidence can be submitted in a manner that is associated with apublic key, in which the bounty hunter 550 proves knowledge of theerror, thereby assigning value (namely the bounty) with the public key.

Assertions made by bounty hunters 550 may be provided directly to miners540 by broadcasting the assertion. Assertions may be broadcast in amanner including, but not limited to posting it to a bulletin board. Insome embodiments of the invention, assertions may be posted to ledgersof blockchains, for instance, the blockchain on which the miners 540operate. If the evidence in question has not been submitted before, thiscan automatically invalidate the ledger entry that is proven wrong andprovide the bounty hunter 550 with some benefit.

Applications and methods in accordance with various embodiments of theinvention are not limited to use within NFT platforms. Accordingly, itshould be appreciated that the capabilities of any blockchainconfiguration described herein can also be implemented outside thecontext of an NFT platform network architecture unrelated to the storageof fungible tokens and/or NFTs. A variety of components, mechanisms, andblockchain configurations that can be utilized within NFT platforms arediscussed further below. Moreover, any of the blockchain configurationsdescribed herein with reference to FIGS. 3-5B (including permissioned,permissionless, and/or hybrid mechanisms) can be utilized within any ofthe networks implemented within the NFT platforms described above.

NFT Platform Consensus Mechanisms

NFT platforms in accordance with many embodiments of the invention candepend on consensus mechanisms to achieve agreement on network state,through proof resolution, to validate transactions. In accordance withmany embodiments of the invention, Proof of Work (PoW) mechanisms may beused as a means of demonstrating non-trivial allocations of processingpower. Proof of Space (PoS) mechanisms may be used as a means ofdemonstrating non-trivial allocations of memory or disk space. As athird possible approach, Proof of Stake mechanisms may be used as ameans of demonstrating non-trivial allocations of fungible tokens and/orNFTs as a form of collateral. Numerous consensus mechanisms are possiblein accordance with various embodiments of the invention, some of whichare expounded on below.

Traditional mining schemes, such as Bitcoin, are based on Proof of Work,based on performing the aforementioned large computational tasks. Thecost of such tasks may not only be computational effort, but also energyexpenditure, a significant environmental concern. To address thisproblem, mining methods operating in accordance with many embodiments ofthe invention may instead operate using Proof of Space mechanisms toaccomplish network consensus, wherein the distinguishing factor can bememory rather than processing power. Specifically, Proof of Spacemechanisms can perform this through network optimization challenges. Inseveral embodiments the network optimization challenge may be selectedfrom any of a number of different challenges appropriate to therequirements of specific applications including graph pebbling. In someembodiments, graph pebbling may refer to a resource allocation gameplayed on discrete mathematics graphs, ending with a labeled graphdisclosing how a player might get at least one pebble to every vertex ofthe graph.

An example of Proof of Work consensus mechanisms that may be implementedin decentralized blockchains, in accordance with a number of embodimentsof the invention, is conceptually illustrated in FIG. 6 . The exampledisclosed in this figure is a challenge-response authentication, aprotocol classification in which one party presents a complex problem(“challenge”) 610 and another party must broadcast a valid answer(“proof”) 620 to have clearance to add a block to the decentralizedledger that makes up the blockchain 630. As a number of miners may becompeting to have this ability, there may be a need for determiningfactors for the addition to be added first, which in this case isprocessing power. Once an output is produced, verifiers 640 in thenetwork can verify the proof, something which typically requires muchless processing power, to determine the first device that would have theright to add the winning block 650 to the blockchain 630. As such, undera Proof of Work consensus mechanism, each miner involved can have asuccess probability proportional to the computational effort expended.

An example of Proof of Space implementations on devices in accordancewith some embodiments of the invention is conceptually illustrated inFIG. 7 . The implementation includes a ledger component 710, a set oftransactions 720, and a challenge 740 computed from a portion of theledger component 710. A representation 715 of a miner's state may alsobe recorded in the ledger component 710 and be publicly available.

In some embodiments, the material stored on the memory of the deviceincludes a collection of nodes 730, 735, where nodes that depend onother nodes have values that are functions of the values of theassociated nodes on which they depend. For example, functions may beone-way functions, such as cryptographic hash functions. In severalembodiments the cryptographic hash function may be selected from any ofa number of different cryptographic hash functions appropriate to therequirements of specific applications including (but not limited to) theSHA1 cryptographic hash function. In such an example, one node in thenetwork may be a function of three other nodes. Moreover, the node maybe computed by concatenating the values associated with these threenodes and applying the cryptographic hash function, assigning the resultof the computation to the node depending on these three parent nodes. Inthis example, the nodes are arranged in rows, where two rows 790 areshown. The nodes are stored by the miner, and can be used to computevalues at a setup time. This can be done using Merkle tree hash-baseddata structures 725, or another structure such as a compression functionand/or a hash function.

Challenges 740 may be processed by the miner to obtain personalizedchallenges 745, made to the device according to the miner's storagecapacity. The personalized challenge 745 can be the same or have anegligible change, but could also undergo an adjustment to account forthe storage space accessible by the miner, as represented by the nodesthe miner stores. For example, when the miner does not have a largeamount of storage available or designated for use with the Proof ofSpace system, a personalized challenge 745 may adjust challenges 740 totake this into consideration, thereby making a personalized challenge745 suitable for the miner's memory configuration.

In some embodiments, the personalized challenge 745 can indicate aselection of nodes 730, denoted in FIG. 7 by filled-in circles. In theFIG. 7 example specifically, the personalized challenge corresponds toone node per row. The collection of nodes selected as a result ofcomputing the personalized challenge 745 can correspond to a validpotential ledger entry 760. However, here a quality value 750 (alsoreferred to herein as a qualifying function value) can also be computedfrom the challenge 740, or from other public information that ispreferably not under the control of any one miner.

A miner may perform matching evaluations 770 to determine whether theset of selected nodes 730 matches the quality value 750. This processcan take into consideration what the memory constraints of the minerare, causing the evaluation 770 to succeed with a greater frequency forlarger memory configurations than for smaller memory configurations.This can simultaneously level the playing field to make the likelihoodof the evaluation 770 succeeding roughly proportional to the size of thememory used to store the nodes used by the miner. In some embodiments,non-proportional relationships may be created by modifying the functionused to compute the quality value 750. When the evaluation 770 resultsin success, then the output value 780 may be used to confirm thesuitability of the memory configuration and validate the correspondingtransaction.

In many embodiments, nodes 730 and 735 can also operate as public keys.The miner may submit valid ledger entries, corresponding to achallenge-response pair including one of these nodes. In that case,public key values can become associated with the obtained NFT. As such,miners can use a corresponding secret/private key to sign transactionrequests, such as purchases. Additionally, any type of digital signaturecan be used in this context, such as RSA signatures, Merkle signatures,DSS signatures, etc. Further, the nodes 730 and 735 may correspond todifferent public keys or to the same public key, the latter preferablyaugmented with a counter and/or other location indicator such as amatrix position indicator, as described above. Location indicators inaccordance with many embodiments of the invention may be applied topoint to locations within a given ledger. In accordance with someembodiments of the invention, numerous Proof of Space consensusconfigurations are possible, some of which are discussed below.

Hybrid methods of evaluating Proof of Space problems can also beimplemented in accordance with many embodiments of the invention. Inmany embodiments, hybrid methods can be utilized that conceptuallycorrespond to modifications of Proof of Space protocols in which extraeffort is expanded to increase the probability of success, or tocompress the amount of space that may be applied to the challenge. Bothcome at a cost of computational effort, thereby allowing miners toimprove their odds of winning by spending greater computational effort.Accordingly, in many embodiments of the invention dual proof-basedsystems may be used to reduce said computational effort. Such systemsmay be applied to Proof of Work and Proof of Space schemes, as well asto any other type of mining-based scheme.

When utilizing dual proofs in accordance with various embodiments of theinvention, the constituent proofs may have varying structures. Forexample, one may be based on Proof of Work, another on Proof of Space,and a third may be a system that relies on a trusted organization forcontrolling the operation, as opposed to relying on mining for theclosing of ledgers. Yet other proof structures can be combined in thisway. The result of the combination will inherit properties of itscomponents. In many embodiments, the hybrid mechanism may incorporate afirst and a second consensus mechanism. In several embodiments, thehybrid mechanism includes a first, a second, and a third consensusmechanisms. In a number of embodiments, the hybrid mechanism includesmore than three consensus mechanisms. Any of these embodiments canutilize consensus mechanisms selected from the group including (but notlimited to) Proof of Work, Proof of Space, and Proof of Stake withoutdeparting from the scope of the invention. Depending on how eachcomponent system is parametrized, different aspects of the inheritedproperties will dominate over other aspects.

Dual proof configurations in accordance with a number of embodiments ofthe invention is illustrated in FIG. 8 . A proof configuration inaccordance with some embodiments of the invention may tend to use thenotion of quality functions for tie-breaking among multiple competingcorrect proofs relative to a given challenge (w) 810. Thisclassification of proof can be described as a qualitative proof,inclusive of proofs of work and proofs of space. In the examplereflected in FIG. 8 , proofs P1 and P2 are each one of a Proof of Work,Proof of Space, Proof of Stake, and/or any other proof related to aconstrained resource, wherein P2 may be of a different type than P1, ormay be of the same type.

Systems in accordance with many embodiments of the invention mayintroduce the notion of a qualifying proof, which, unlike qualitativeproofs, are either valid or not valid, using no tie-breaking mechanism.Said systems may include a combination of one or more qualitative proofsand one or more qualifying proofs. For example, it may use onequalitative proof that is combined with one qualifying proof, where thequalifying proof is performed conditional on the successful creation ofa qualitative proof. FIG. 8 illustrates challenge w 810, as describedabove, with a function 1 815, which is a qualitative function, andfunction 2 830, which is a qualifying function.

To stop miners from expending effort after a certain amount of efforthas been spent, thereby reducing the environmental impact of mining,systems in accordance with a number of embodiments of the invention canconstrain the search space for the mining effort. This can be done usinga configuration parameter that controls the range of random orpseudo-random numbers that can be used in a proof. Upon challenge w 810being issued to one or more miners 800, it can be input to Function 1815 along with configuration parameter C1 820. Function 1 815 may outputproof P1 825, in this example the qualifying proof to Function 2 830.Function 2 830 is also provided with configuration parameter C2 840 andcomputes qualifying proof P2 845. The miner 800 can then submit thecombination of proofs (P1, P2) 850 to a verifier, in order to validate aledger associated with challenge w 810. In some embodiments, miner 800can also submit the proofs (P1, P2) 850 to be accessed by a 3rd-partyverifier.

NFT platforms in accordance with many embodiments of the invention mayadditionally benefit from alternative energy-efficient consensusmechanisms. Therefore, computer systems in accordance with severalembodiments of the invention may instead use consensus-based methodsalongside or in place of proof-of-space and proof-of-space based mining.In particular, consensus mechanisms based instead on the existence of aTrusted Execution Environment (TEE), such as ARM TrustZone™ or IntelSGX™ may provide assurances exist of integrity by virtue ofincorporating private/isolated processing environments.

An illustration of sample process 900 undergone by TEE-based consensusmechanisms in accordance with some embodiments of the invention isdepicted in FIG. 9 . In some such configurations, a setup 910 may beperformed by an original equipment manufacturer (OEM) or a partyperforming configurations of equipment provided by an OEM. Once aprivate key/public key pair is generated in the secure environment,process 900 may store (920) the private key in TEE storage (i.e. storageassociated with the Trusted Execution Environment). While storage may beaccessible from the TEE, it can be shielded from applications runningoutside the TEE. Additionally, processes can store (930) the public keyassociated with the TEE in any storage associated with the devicecontaining the TEE. Unlike the private key, the public key may also beaccessible from applications outside the TEE. In a number ofembodiments, the public key may also be certified. Certification maycome from OEMs or trusted entities associated with the OEMs, wherein thecertificate can be stored with the public key.

In many embodiments of the invention, mining-directed steps can also beinfluenced by the TEE. In the illustrated embodiment, the process 900can determine (950) a challenge. For example, this may be by computing ahash of the contents of a ledger. In doing so, process 900 may alsodetermine whether the challenge corresponds to success 960. In someembodiments of the invention, the determination of success may resultfrom some pre-set portion of the challenge matching a pre-set portion ofthe public key, e.g. the last 20 bits of the two values matching. Inseveral embodiments the success determination mechanism may be selectedfrom any of a number of alternate approaches appropriate to therequirements of specific applications. The matching conditions may alsobe modified over time. For example, modification may result from anannouncement from a trusted party or based on a determination of anumber of participants having reached a threshold value.

When the challenge does not correspond to a success 960, process 900 canreturn to determine (950) a new challenge. In this context, process 900can determine (950) a new challenge after the ledger contents have beenupdated and/or a time-based observation is performed. In severalembodiments the determination of a new challenge may come from any of anumber of approaches appropriate to the requirements of specificapplications; including, but not limited to, the observation of as asecond elapsing since the last challenge. If the challenge correspondsto a success 960, then the processing can continue on to access (970)the private key using the TEE.

When the private key is accessed, process can generate (980) a digitalsignature using the TEE. The digital signature may be on a message thatincludes the challenge and/or which otherwise references the ledgerentry being closed. Process 900 can also transmit (980) the digitalsignature to other participants implementing the consensus mechanism. Incases where multiple digital signatures are received and found to bevalid, a tie-breaking mechanism can be used to evaluate the consensus.For example, one possible tie-breaking mechanism may be to select thewinner as the party with the digital signature that represents thesmallest numerical value when interpreted as a number. In severalembodiments the tie-breaking mechanism may be selected from any of anumber of alternate tie-breaking mechanisms appropriate to therequirements of specific applications.

Applications and methods in accordance with various embodiments of theinvention are not limited to use within NFT platforms. Accordingly, itshould be appreciated that consensus mechanisms described herein canalso be implemented outside the context of an NFT platform networkarchitecture unrelated to the storage of fungible tokens and/or NFTs.Moreover, any of the consensus mechanisms described herein withreference to FIGS. 6-9 (including Proof of Work, Proof of Space, proofof stake, and/or hybrid mechanisms) can be utilized within any of theblockchains implemented within the NFT platforms described above withreference to FIGS. 3-5B. Various systems and methods for implementingNFT platforms and applications in accordance with numerous embodimentsof the invention are discussed further below.

NFT Platform Constituent Devices and Applications

A variety of computer systems that can be utilized within NFT platformsand systems that utilize NFT blockchains in accordance with variousembodiments of the invention are illustrated below. The computer systemsin accordance with many embodiments of the invention may implement aprocessing system 1010, 1120, 1220 using one or more CPUs, GPUs, ASICs,FPGAs, and/or any of a variety of other devices and/or combinations ofdevices that are typically utilized to perform digital computations. Ascan readily be appreciated each of these computer systems can beimplemented using one or more of any of a variety of classes ofcomputing devices including (but not limited to) mobile phone handsets,tablet computers, laptop computers, personal computers, gaming consoles,televisions, set top boxes and/or other classes of computing device.

A user device capable of communicating with an NFT platform inaccordance with an embodiment of the invention is illustrated in FIG. 10. The memory system 1040 of particular user devices may include anoperating system 1050 and media wallet applications 1060. Media walletapplications may include sets of media wallet (MW) keys 1070 that caninclude public key/private key pairs. The set of MW keys may be used bythe media wallet application to perform a variety of actions including,but not limited to, encrypting and signing data. In many embodiments,the media wallet application enables the user device to obtain andconduct transactions with respect to NFTs by communicating with an NFTblockchain via the network interface 1030. In some embodiments, themedia wallet applications are capable of enabling the purchase of NFTsusing fungible tokens via at least one distributed exchange. Userdevices may implement some or all of the various functions describedabove with reference to media wallet applications as appropriate to therequirements of a given application in accordance with variousembodiments of the invention.

A verifier 1110 capable of verifying blockchain transactions in an NFTplatform in accordance with many embodiments of the invention isillustrated in FIG. 11 . The memory system 1160 of the verifier computersystem includes an operating system 1140 and a verifier application 1150that enables the verifier 1110 computer system to access a decentralizedblockchain in accordance with various embodiments of the invention.Accordingly, the verifier application 1150 may utilize a set of verifierkeys 1170 to affirm blockchain entries. When blockchain entries can beverified, the verifier application 1150 may transmit blocks to thecorresponding blockchains. The verifier application 1150 can alsoimplement some or all of the various functions described above withreference to verifiers as appropriate to the requirements of a givenapplication in accordance with various embodiments of the invention.

A content creator system 1210 capable of disseminating content in an NFTplatform in accordance with an embodiment of the invention isillustrated in FIG. 12 . The memory system 1260 of the content creatorcomputer system may include an operating system 1240 and a contentcreator application 1250. The content creator application 1250 mayenable the content creator computer system to mint NFTs by writing smartcontracts to blockchains via the network interface 1230. The contentcreator application can include sets of content creator wallet (CCW)keys 1270 that can include a public key/private key pairs. Contentcreator applications may use these keys to sign NFTs minted by thecontent creator application. The content creator application can alsoimplement some or all of the various functions described above withreference to content creators as appropriate to the requirements of agiven application in accordance with various embodiments of theinvention.

Computer systems in accordance with many embodiments of the inventionincorporate digital wallets (herein also referred to as “wallets” or“media wallets”) for NFT and/or fungible token storage. In severalembodiments, the digital wallet may securely store rich media NFTsand/or other tokens. Additionally, in some embodiments, the digitalwallet may display user interface through which user instructionsconcerning data access permissions can be received.

In a number of embodiments of the invention, digital wallets may be usedto store at least one type of token-directed content. Example contenttypes may include, but are not limited to crypto currencies of one ormore sorts; non-fungible tokens; and user profile data.

Example user profile data may incorporate logs of user actions. Inaccordance with some embodiments of the invention, example anonymizeduser profile data may include redacted, encrypted, and/or otherwiseobfuscated user data. User profile data in accordance with someembodiments may include, but are not limited to, information related toclassifications of interests, determinations of a post-advertisementpurchases, and/or characterizations of wallet contents.

Media wallets, when storing content, may store direct references tocontent. Media wallets may also reference content through keys todecrypt and/or access the content. Media wallets may use such keys toadditionally access metadata associated with the content. Examplemetadata may include, but is not limited to, classifications of content.In a number of embodiments, the classification metadata may governaccess rights of other parties related to the content.

Access governance rights may include, but are not limited to, whether aparty can indicate their relationship with the wallet; whether they canread summary data associated with the content; whether they have accessto peruse the content; whether they can place bids to purchase thecontent; whether they can borrow the content, and/or whether they arebiometrically authenticated.

An example of a media wallet 1310 capable of storing rich media NFTs inaccordance with an embodiment of the invention is illustrated in FIG. 13. Media wallets 1310 may include a storage component 1330, includingaccess right information 1340, user credential information 1350, tokenconfiguration data 1360, and/or at least one private key 1370. Inaccordance with many embodiments of the invention, a private key 1370may be used to perform a plurality of actions on resources, includingbut not limited to decrypting NFT and/or fungible token content. Mediawallets may also correspond to a public key, referred to as a walletaddress. An action performed by private keys 1370 may be used to proveaccess rights to digital rights management modules. Additionally,private keys 1370 may be applied to initiating ownership transfers andgranting NFT and/or fungible token access to alternate wallets. Inaccordance with some embodiments, access right information 1340 mayinclude lists of elements that the wallet 1310 has access to. Accessright information 1340 may also express the type of access provided tothe wallet. Sample types of access include, but are not limited to, theright to transfer NFT and/or fungible ownership, the right to play richmedia associated with a given NFT, and the right to use an NFT and/orfungible token. Different rights may be governed by differentcryptographic keys. Additionally, the access right information 1340associated with a given wallet 1310 may utilize user credentialinformation 1350 from the party providing access.

In accordance with many embodiments of the invention, third partiesinitiating actions corresponding to requesting access to a given NFT mayrequire user credential information 1350 of the party providing accessto be verified. User credential information 1350 may be taken from thegroup including, but not limited to, a digital signature, hashedpasswords, PINs, and biometric credentials. User credential information1350 may be stored in a manner accessible only to approved devices. Inaccordance with some embodiments of the invention, user credentialinformation 1350 may be encrypted using a decryption key held by trustedhardware, such as a trusted execution environment. Upon verification,user credential information 1350 may be used to authenticate walletaccess.

Available access rights may be determined by digital rights management(DRM) modules 1320 of wallets 1310. In the context of rich media,encryption may be used to secure content. As such, DRM systems may referto technologies that control the distribution and use of keys requiredto decrypt and access content. DRM systems in accordance with manyembodiments of the invention may require a trusted execution zone.Additionally, said systems may require one or more keys (typically acertificate containing a public key/private key pair) that can be usedto communicate with and register with DRM servers. DRM modules 1320 insome embodiments may also use one or more keys to communicate with a DRMserver. In several embodiments, the DRM modules 1320 may include codeused for performing sensitive transactions for wallets including, butnot limited to, content access. In accordance with a number ofembodiments of the invention, the DRM module 1320 may execute in aTrusted Execution Environment. In a number of embodiments, the DRM maybe facilitated by an Operating System (OS) that enables separation ofprocesses and processing storage from other processes and theirprocessing storage.

Operation of media wallet applications implemented in accordance withsome embodiments of the invention is conceptually illustrated by way ofthe user interfaces shown in FIGS. 14A-14C. In many embodiments, mediawallet applications can refer to applications that are installed uponuser devices such as (but not limited to) mobile phones and tabletcomputers running the iOS, Android and/or similar operating systems.Launching media wallet applications can provide a number of userinterface contexts. In many embodiments, transitions between these userinterface contexts can be initiated in response to gestures including(but not limited to) swipe gestures received via a touch user interface.As can readily be appreciated, the specific manner in which userinterfaces operate through media wallet applications is largelydependent upon the user input capabilities of the underlying userdevice. In several embodiments, a first user interface context is adashboard (see, FIGS. 14A, 14C) that can include a gallery view of NFTsowned by the user. In several embodiments, the NFT listings can beorganized into category index cards. Category index cards may include,but are not limited to digital merchandise/collectibles, special eventaccess/digital tickets, fan leaderboards. In certain embodiments, asecond user interface context (see, for example, FIG. 14B) may displayindividual NFTs. In a number of embodiments, each NFT can be main-stagedin said display with its status and relevant information shown. Userscan swipe through each collectible and interacting with the userinterface can launch a collectible user interface enabling greaterinteraction with a particular collectible in a manner that can bedetermined based upon the smart contract underlying the NFT.

A participant of an NFT platform may use a digital wallet to classifywallet content, including NFTs, fungible tokens, content that is notexpressed as tokens such as content that has not yet been minted but forwhich the wallet can initiate minting, and other non-token content,including (but not limited to) executable content, webpages,configuration data, history files and logs. This classification may beperformed using a visual user interface. Users interface may enableusers to create a visual partition of a space. In some embodiments ofthe invention, a visual partition may in turn be partitioned intosub-partitions. In some embodiments, a partition of content may separatewallet content into content that is not visible to the outside world(“invisible partition”), and content that is visible at least to someextent by the outside world (“visible partition”). Some of the walletcontent may require the wallet use to have an access code such as apassword or a biometric credential to access, view the existence of, orperform transactions on the wallet content. A visible partition may besubdivided into two or more partitions, where the first one correspondsto content that can be seen by anybody, the second partition correspondsto content that can be seen by members of a first group, and/or thethird partition corresponds to content that can be seen by members of asecond group.

For example, the first group may be users with which the user hascreated a bond, and invited to be able to see content. The second groupmay be users who have a membership and/or ownership that may not becontrolled by the user. An example membership may be users who ownnon-fungible tokens (NFTs) from a particular content creator. Contentelements, through icons representing the elements, may be relocated intovarious partitions of the space representing the user wallet. By doingso, content elements may be associated with access rights governed byrules and policies of the given partition.

One additional type of visibility may be partial visibility. Partialvisibility can correspond to a capability to access metadata associatedwith an item, such as an NFT and/or a quantity of crypto funds, but notcarry the capacity to read the content, lend it out, or transferownership of it. As applied to a video NFT, an observer to a partitionwith partial visibility may not be able to render the video beingencoded in the NFT but see a still image of it and a descriptionindicating its source.

Similarly, a party may have access to a first anonymized profile whichstates that the user associated with the wallet is associated with agiven demographic. The party with this access may also be able todetermine that a second anonymized profile including additional data isavailable for purchase. This second anonymized profile may be kept in asub-partition to which only people who pay a fee have access, therebyexpressing a form of membership. Alternatively, only users that haveagreed to share usage logs, aspects of usage logs or parts thereof maybe allowed to access a given sub-partition. By agreeing to share usagelog information with the wallet comprising the sub-partition, thiswallet learns of the profiles of users accessing various forms ofcontent, allowing the wallet to customize content, including byincorporating advertisements, and to determine what content to acquireto attract users of certain demographics.

Another type of membership may be held by advertisers who have sentpromotional content to the user. These advertisers may be allowed toaccess a partition that stores advertisement data. Such advertisementdata may be encoded in the form of anonymized profiles. In a number ofembodiments, a given sub-partition may be accessible only to theadvertiser to whom the advertisement data pertains. Elements describingadvertisement data may be automatically placed in their associatedpartitions, after permission has been given by the user. This partitionmay either be visible to the user. Visibility may also depend on adirect request to see “system partitions.”

The placing of content in a given partition may be performed by adrag-and-drop action performed on a visual interface. By selecting itemsand clusters and performing a drag-and-drop to another partition and/orto a sub-partition, the visual interface may allow movement including,but not limited to, one item, a cluster of items, and a multiplicity ofitems and clusters of items. The selection of items can be performedusing a lasso approach in which items and partitions are circled as theyare displayed. The selection of items may also be performed byalternative methods for selecting multiple items in a visual interface,as will be appreciated by a person of skill in the art.

Some content classifications may be automated in part or full. Forexample, when user place ten artifacts, such as NFTs describing in-gamecapabilities, in a particular partition, they may be asked if additionalcontent that are also in-game capabilities should be automaticallyplaced in the same partition as they are acquired and associated withthe wallet. When “yes” is selected, then this placement may be automatedin the future. When “yes, but confirm for each NFT” is selected, thenusers can be asked, for each automatically classified element, toconfirm its placement. Before the user confirms, the element may remainin a queue that corresponds to not being visible to the outside world.When users decline given classifications, they may be asked whetheralternative classifications should be automatically performed for suchelements onwards. In some embodiments, the selection of alternativeclassifications may be based on manual user classification taking placesubsequent to the refusal.

Automatic classification of elements may be used to perform associationswith partitions and/or folders. The automatic classification may bebased on machine learning (ML) techniques considering characteristicsincluding, but not limited to, usage behaviors exhibited by the userrelative to the content to be classified, labels associated with thecontent, usage statistics; and/or manual user classifications of relatedcontent.

Multiple views of wallets may also be accessible. One such view cancorrespond to the classifications described above, which indicates theactions and interactions others can perform relative to elements.Another view may correspond to a classification of content based on use,type, and/or users-specified criterion. For example, all game NFTs maybe displayed in one collection view. The collection view may furthersubdivide the game NFTs into associations with different games orcollections of games. Another collection may show all audio content,clustered based on genre. users-specified classification may be whetherthe content is for purposes of personal use, investment, or both. Acontent element may show up in multiple views. users can search thecontents of his or her wallet by using search terms that result inpotential matches.

Alternatively, the collection of content can be navigated based thedescribed views of particular wallets, allowing access to content. Oncea content element has been located, the content may be interacted with.For example, located content elements may be rendered. One view may beswitched to another after a specific item is found. For example, thismay occur through locating an item based on its genre and after the itemis found, switching to the partitioned view described above. In someembodiments, wallet content may be rendered using two or more views in asimultaneous manner. They may also select items using one view.

Media wallet applications in accordance with various embodiments of theinvention are not limited to use within NFT platforms. Accordingly, itshould be appreciated that applications described herein can also beimplemented outside the context of an NFT platform network architectureunrelated to the storage of fungible tokens and/or NFTs. Moreover, anyof the computer systems described herein with reference to FIGS. 10-14Ccan be utilized within any of the NFT platforms described above.

NFT Platform NFT Interactions

NFT platforms in accordance with many embodiments of the invention mayincorporate a wide variety of rich media NFT configurations. The term“Rich Media Non-Fungible Tokens” can be used to refer toblockchain-based cryptographic tokens created with respect to a specificpiece of rich media content and which incorporate programmaticallydefined digital rights management. In some embodiments of the invention,each NFT may have a unique serial number and be associated with a smartcontract defining an interface that enables the NFT to be managed, ownedand/or traded.

Under a rich media blockchain in accordance with many embodiments of theinvention, a wide variety of NFT configurations may be implemented. SomeNFTs may be referred to as anchored NFTs (or anchored tokens), used totie some element, such as a physical entity, to an identifier. Of thisclassification, one sub-category may be used to tie users' real-worldidentities and/or identifiers to a system identifier, such as a publickey. In this disclosure, this type of NFT applied to identifying users,may be called a social NFT, identity NFT, identity token, and a socialtoken. In accordance with many embodiments of the invention, anindividual's personally identifiable characteristics may be contained,maintained, and managed throughout their lifetime so as to connect newinformation and/or NFTs to the individual's identity. A social NFT'sinformation may include, but are not limited to, personally identifiablecharacteristics such as name, place and date of birth, and/orbiometrics.

An example social NFT may assign a DNA print to a newborn's identity. Inaccordance with a number of embodiments of the invention, this firstsocial NFT might then be used in the assignment process of a socialsecurity number NFT from the federal government. In some embodiments,the first social NFT may then be associated with some rights andcapabilities, which may be expressed in other NFTs. Additional rightsand capabilities may also be directly encoded in a policy of the socialsecurity number NFT.

A social NFT may exist on a personalized branch of a centralized and/ordecentralized blockchain. Ledger entries related to an individual'ssocial NFT in accordance with several embodiments of the invention aredepicted in FIG. 15 . Ledger entries of this type may be used to buildan immutable identity foundation whereby biometrics, birth and parentalinformation are associated with an NFT. As such, this information mayalso be protected with encryption using a private key 1530. The initialentry in a ledger, “ledger entry 0” 1505, may represent a social token1510 assignment to an individual with a biometric “A” 1515. In thisembodiment, the biometric may include but is not limited to a footprint,a DNA print, and a fingerprint. The greater record may also include theindividual's date and time of birth 1520 and place of birth 1525. Asubsequent ledger entry 1 1535 may append parental information includingbut not limited to mothers' name 1540, mother's social token 1545,father's name 1550, and father's social token 1555.

In a number of embodiments, the various components that make up a socialNFT may vary from situation to situation. In a number of embodiments,biometrics and/or parental information may be unavailable in a givensituation and/or period of time. Other information including, but notlimited to, race gender, and governmental number assignments such associal security numbers, may be desirable to include in the ledger. In ablockchain, future NFT creation may create a life-long ledger record ofan individual's public and private activities. In accordance with someembodiments, the record may be associated with information including,but not limited to, identity, purchases, health and medical records,access NFTs, family records such as future offspring, marriages,familial history, photographs, videos, tax filings, and/or patentfilings. The management and/or maintenance of an individual's biometricsthroughout the individual's life may be immutably connected to the firstsocial NFT given the use of a decentralized blockchain ledger.

In some embodiments, a certifying third party may generate an NFTassociated with certain rights upon the occurrence of a specific event.In one such embodiment, the DMV may be the certifying party and generatean NFT associated with the right to drive a car upon issuing atraditional driver's license. In another embodiment, the certifyingthird party may be a bank that verifies a person's identity papers andgenerates an NFT in response to a successful verification. In a thirdembodiment, the certifying party may be a car manufacturer, whogenerates an NFT and associates it with the purchase and/or lease of acar.

In many embodiments, a rule may specify what types of policies thecertifying party may associate with the NFT. Additionally, anon-certified entity may also generate an NFT and assert its validity.This may require putting up some form of security. In one example,security may come in the form of a conditional payment associated withthe NFT generated by the non-certified entity. In this case, theconditional payment may be exchangeable for funds if abuse can bedetected by a bounty hunter and/or some alternate entity. Non-certifiedentities may also relate to a publicly accessible reputation recorddescribing the non-certified entity's reputability.

Anchored NFTs may additionally be applied to automatic enforcement ofprogramming rules in resource transfers. NFTs of this type may bereferred to as promise NFTs. A promise NFT may include an agreementexpressed in a machine-readable form and/or in a human-accessible form.In a number of embodiments, the machine-readable and human-readableelements can be generated one from the other. In some embodiments, anagreement in a machine-readable form may include, but is not limited to,a policy and/or an executable script. In some embodiments, an agreementin a human-readable form may include, but is not limited to, a textand/or voice-based statement of the promise.

In some embodiments, regardless of whether the machine-readable andhuman-readable elements are generated from each other, one can beverified based on the other. Smart contracts including bothmachine-readable statements and human-accessible statements may also beused outside the implementation of promise NFTs. Moreover, promise NFTsmay be used outside actions taken by individual NFTs and/or NFT-owners.In some embodiments, promise NFTs may relate to general conditions, andmay be used as part of a marketplace.

In one such example, horse betting may be performed through generating afirst promise NFT that offers a payment of $10 if a horse does not win.Payment may occur under the condition that the first promise NFT ismatched with a second promise NFT that causes a transfer of funds to apublic key specified with the first promise NFT if horse X wins.

A promise NFT may be associated with actions that cause the execution ofa policy and/or rule indicated by the promise NFT. In some embodimentsof the invention, a promise of paying a charity may be associated withthe sharing of an NFT. In this embodiment, the associated promise NFTmay identify a situation that satisfies the rule associated with thepromise NFT, thereby causing the transfer of funds when the condition issatisfied (as described above). One method of implementation may beembedding in and/or associating a conditional payment with the promiseNFT. A conditional payment NFT may induce a contract causing thetransfer of funds by performing a match. In some such methods, the matchmay be between the promise NFT and inputs that identify that theconditions are satisfied, where said input can take the form of anotherNFT. In a number of embodiments, one or more NFTs may also relate toinvestment opportunities.

For example, a first NFT may represent a deed to a first building, and asecond NFT a deed to a second building. Moreover, the deed representedby the first NFT may indicate that a first party owns the firstproperty. The deed represented by the second NFT may indicate that asecond party owns the second property. A third NFT may represent one ormore valuations of the first building. The third NFT may in turn beassociated with a fourth NFT that may represent credentials of a partyperforming such a valuation. A fifth NFT may represent one or morevaluations of the second building. A sixth may represent the credentialsof one of the parties performing a valuation. The fourth and sixth NFTsmay be associated with one or more insurance policies, asserting that ifthe parties performing the valuation are mistaken beyond a specifiederror tolerance, then the insurer would pay up to a specified amount.

A seventh NFT may then represent a contract that relates to the plannedacquisition of the second building by the first party, from the secondparty, at a specified price. The seventh NFT may make the contractconditional provided a sufficient investment and/or verification by athird party. A third party may evaluate the contract of the seventh NFT,and determine whether the terms are reasonable. After the evaluation,the third party may then verify the other NFTs to ensure that the termsstated in the contract of the seventh NFT agree. If the third partydetermines that the contract exceeds a threshold in terms of value torisk, as assessed in the seventh NFT, then executable elements of theseventh NFT may cause transfers of funds to an escrow party specified inthe contract of the sixth NFT.

Alternatively, the first party may initiate the commitment of funds,conditional on the remaining funds being raised within a specified timeinterval. The commitment of funds may occur through posting thecommitment to a ledger. Committing funds may produce smart contractsthat are conditional on other events, namely the payments needed tocomplete the real estate transaction. The smart contract also may haveone or more additional conditions associated with it. For example, anadditional condition may be the reversal of the payment if, after aspecified amount of time, the other funds have not been raised. Anothercondition may be related to the satisfactory completion of an inspectionand/or additional valuation.

NFTs may also be used to assert ownership of virtual property. Virtualproperty in this instance may include, but is not limited to, rightsassociated with an NFT, rights associated with patents, and rightsassociated with pending patents. In a number of embodiments, theentities involved in property ownership may be engaged in fractionalownership. In some such embodiments, two parties may wish to purchase anexpensive work of digital artwork represented by an NFT. The parties canenter into smart contracts to fund and purchase valuable works. After apurchase, an additional NFT may represent each party's contribution tothe purchase and equivalent fractional share of ownership.

Another type of NFTs that may relate to anchored NFTs may be called“relative NFTs.” This may refer to NFTs that relate two or more NFTs toeach other. Relative NFTs associated with social NFTs may includedigital signatures that is verified using a public key of a specificsocial NFT. In some embodiments, an example of a relative NFT may be anassertion of presence in a specific location, by a person correspondingto the social NFT. This type of relative NFT may also be referred to asa location NFT and a presence NFT. Conversely, a signature verifiedusing a public key embedded in a location NFT may be used as proof thatan entity sensed by the location NFT is present. Relative NFTs arederived from other NFTs, namely those they relate to, and therefore mayalso be referred to as derived NFTs. An anchored NFT may tie to anotherNFT, which may make it both anchored and relative. An example of suchmay be called pseudonym NFTs.

Pseudonym NFTs may be a kind of relative NFT acting as a pseudonymidentifier associated with a given social NFT. In some embodiments,pseudonym NFTs may, after a limited time and/or a limited number oftransactions, be replaced by a newly derived NFTs expressing newpseudonym identifiers. This may disassociate users from a series ofrecorded events, each one of which may be associated with differentpseudonym identifiers. A pseudonym NFT may include an identifier that isaccessible to biometric verification NFTs. Biometric verification NFTsmay be associated with a TEE and/or DRM which is associated with one ormore biometric sensors. Pseudonym NFTs may be output by social NFTsand/or pseudonym NFTs.

Inheritance NFTs may be another form of relative NFTs, that transfersrights associated with a first NFT to a second NFT. For example,computers, represented by an anchored NFT that is related to a physicalentity (the hardware), may have access rights to WiFi networks. Whencomputers are replaced with newer models, users may want to maintain allold relationships, for the new computer. For example, users may want toretain WiFi hotspots. For this to be facilitated, a new computer can berepresented by an inheritance NFT, inheriting rights from the anchoredNFT related to the old computer. An inheritance NFT may acquire some orall pre-existing rights associated with the NFT of the old computer, andassociate those with the NFT associated with the new computer.

More generally, multiple inheritance NFTs can be used to selectivelytransfer rights associated with one NFT to one or more NFTs, where suchNFTs may correspond to users, devices, and/or other entities, when suchassignments of rights are applicable. Inheritance NFTs can also be usedto transfer property. One way to implement the transfer of property canbe to create digital signatures using private keys. These private keysmay be associated with NFTs associated with the rights. In accordancewith a number of embodiments, transfer information may include theassignment of included rights, under what conditions the transfer mayhappen, and to what NFT(s) the transfer may happen. In this transfer,the assigned NFTs may be represented by identifies unique to these, suchas public keys. The digital signature and message may then be in theform of an inheritance NFT, or part of an inheritance NFT. As rights areassigned, they may be transferred away from previous owners to newowners through respective NFTs. Access to financial resources is onesuch example.

However, sometimes rights may be assigned to new parties without takingthe same rights away from the party (i.e., NFT) from which the rightscome. One example of this may be the right to listen to a song, when alicense to the song is sold by the artist to consumers. However, if theseller sells exclusive rights, this causes the seller not to have therights anymore.

In accordance with many embodiments of the invention, multiplealternative NFT configurations may be implemented. One classification ofNFT may be an employee NFT or employee token. Employee NFTs may be usedby entities including, but not limited to, business employees, students,and organization members. Employee NFTs may operate in a manneranalogous to key card photo identifications. In a number of embodiments,employee NFTs may reference information including, but not limited to,company information, employee identity information and/or individualidentity NFTs.

Additionally, employee NFTs may include associated access NFTinformation including but not limited to, what portions of a buildingemployees may access, and what computer system employees may utilize. Inseveral embodiments, employee NFTs may incorporate their owner'sbiometrics, such as a face image. In a number of embodiments, employeeNFTs may operate as a form of promise NFT. In some embodiments, employeeNFT may comprise policies or rules of employing organization. In anumber of embodiments, the employee NFT may reference a collection ofother NFTs.

Another type of NFT may be referred to as the promotional NFT orpromotional token. Promotional NFTs may be used to provide verificationthat promoters provide promotion winners with promised goods. In someembodiments, promotional NFTs may operate through decentralizedapplications for which access restricted to those using an identity NFT.The use of a smart contract with a promotional NFT may be used to allowfor a verifiable release of winnings. These winnings may include, butare not limited to, cryptocurrency, money, and gift card NFTs useful topurchase specified goods. Smart contracts used alongside promotionalNFTs may be constructed for winners selected through random numbergeneration.

Another type of NFT may be called the script NFT or script token. Scripttokens may incorporate script elements including, but not limited to,story scripts, plotlines, scene details, image elements, avatar models,sound profiles, and voice data for avatars. Script tokens may alsoutilize rules and policies that describe how script elements arecombined. Script tokens may also include rightsholder information,including but not limited to, licensing and copyright information.Executable elements of script tokens may include instructions for how toprocess inputs; how to configure other elements associated with thescript tokens; and how to process information from other tokens used incombination with script tokens.

Script tokens may be applied to generate presentations of information.In accordance with some embodiments, these presentations may bedeveloped on devices including but not limited to traditional computers,mobile computers, and virtual reality display devices. Script tokens maybe used to provide the content for game avatars, digital assistantavatars, and/or instructor avatars. Script tokens may compriseaudio-visual information describing how input text is presented, alongwith the input text that provides the material to be presented. It mayalso comprise what may be thought of as the personality of the avatar,including how the avatar may react to various types of input from anassociated user.

In some embodiments, script NFTs may be applied to govern behaviorwithin an organization. For example, this may be done through digitalsignatures asserting the provenance of the scripts. Script NFTs mayalso, in full and/or in part, be generated by freelancers. For example,a text script related to a movie, an interactive experience, a tutorial,and/or other material, may be created by an individual content creator.This information may then be combined with a voice model or avatar modelcreated by an established content producer. The information may then becombined with a background created by additional parties. Variouscontent producers can generate parts of the content, allowing forlarge-scale content collaboration.

Features of other NFTs can be incorporated in a new NFT using techniquesrelated to inheritance NFTs, and/or by making references to other NFTs.As script NFTs may consist of multiple elements, creators with specialskills related to one particular element may generate and combineelements. This may be used to democratize not only the writing ofstorylines for content, but also outsourcing for content production. Foreach such element, an identifier establishing the origin or provenanceof the element may be included. Policy elements can also be incorporatedthat identify the conditions under which a given script element may beused. Conditions may be related to, but are not limited to executionenvironments, trusts, licenses, logging, financial terms for use, andvarious requirements for the script NFTs. Requirements may concern, butare not limited to, what other types of elements the given element arecompatible with, what is allowed to be combined with according the termsof service, and/or local copyright laws that must be obeyed.

Evaluation units may be used with various NFT classifications to collectinformation on their use. Evaluation units may take a graph representingsubsets of existing NFTs and make inferences from the observed graphcomponent. From this, valuable insights into NFT value may be derived.For example, evaluation units may be used to identify NFTs whosepopularity is increasing or waning. In that context, popularity may beexpressed as, but not limited to, the number of derivations of the NFTthat are made; the number of renderings, executions or other uses aremade; and the total revenue that is generated to one or more partiesbased on renderings, executions or other uses.

Evaluation units may make their determination through specific windowsof time and/or specific collections of end-users associated with theconsumption of NFT data in the NFTs. Evaluation units may limitassessments to specific NFTs (e.g. script NFTs). This may be applied toidentify NFTs that are likely to be of interest to various users. Inaddition, the system may use rule-based approaches to identify NFTs ofimportance, wherein importance may be ascribed to, but is not limitedto, the origination of the NFTs, the use of the NFTs, the velocity ofcontent creation of identified clusters or classes, the actions taken byconsumers of NFT, including reuse of NFTs, the lack of reuse of NFTs,and the increased or decreased use of NFTs in selected social networks.

Evaluations may be repurposed through recommendation mechanisms forindividual content consumers and/or as content originators. Anotherexample may address the identification of potential combinationopportunities, by allowing ranking based on compatibility. Accordingly,content creators such as artists, musicians and programmers can identifyhow to make their content more desirable to intended target groups.

The generation of evaluations can be supported by methods including, butnot limited to machine learning (ML) methods, artificial intelligence(AI) methods, and/or statistical methods. Anomaly detection methodsdeveloped to identify fraud can be repurposed to identify outliers. Thiscan be done to flag abuse risks or to improve the evaluation effort.

Multiple competing evaluation units can make competing predictions usingalternative and proprietary algorithms. Thus, different evaluation unitsmay be created to identify different types of events to different typesof subscribers, monetizing their insights related to the data theyaccess.

In a number of embodiments, evaluation units may be a form of NFTs thatderive insights from massive amounts of input data. Input data maycorrespond, but is not limited to the graph component being analyzed.Such NFTs may be referred to as evaluation unit NFTs.

The minting of NFTs may associate rights with first owners and/or withan optional one or more policies and protection modes. An example policyand/or protection mode directed to financial information may expressroyalty requirements. An example policy and/or protection mode directedto non-financial requirements may express restrictions on access and/orreproduction. An example policy directed to data collection may expresslistings of user information that may be collected and disseminated toother participants of the NFT platform.

An example NFT which may be associated with specific content inaccordance with several embodiments of the invention is illustrated inFIG. 16 . In some embodiments, an NFT 1600 may utilize a vault 1650,which may control access to external data storage areas. Methods ofcontrolling access may include, but are not limited to, user credentialinformation 1350. In accordance with a number of embodiments of theinvention, control access may be managed through encrypting content1640. As such, NFTs 1600 can incorporate content 1640, which may beencrypted, not encrypted, yet otherwise accessible, or encrypted inpart. In accordance with some embodiments, an NFT 1600 may be associatedwith one or more content 1640 elements, which may be contained in orreferenced by the NFT. A content 1640 element may include, but is notlimited to, an image, an audio file, a script, a biometric useridentifier, and/or data derived from an alternative source. An examplealternative source may be a hash of biometric information). An NFT 1600may also include an authenticator 1620 capable of affirming thatspecific NFTs are valid.

In accordance with many embodiments of the invention, NFTs may include anumber of rules and policies 1610. Rules and policies 1610 may include,but are not limited to access rights information 1340. In someembodiments, rules and policies 1610 may also state terms of usage,royalty requirements, and/or transfer restrictions. An NFT 1600 may alsoinclude an identifier 1630 to affirm ownership status. In accordancewith many embodiments of the invention, ownership status may beexpressed by linking the identifier 1630 to an address associated with ablockchain entry.

In accordance with a number of embodiments of the invention, NFTs mayrepresent static creative content. NFTs may also be representative ofdynamic creative content, which changes over time. In accordance withmany examples of the invention, the content associated with an NFT maybe a digital content element.

One example of a digital content element in accordance with someembodiments may be a set of five images of a mouse. In this example, thefirst image may be an image of the mouse being alive. The second may bean image of the mouse eating poison. The third may be an image of themouse not feeling well. The fourth image may be of the mouse, dead. Thefifth image may be of a decaying mouse.

The user credential information 1350 of an NFT may associate each imageto an identity, such as of the artist. In accordance with a number ofembodiments of the invention, NFT digital content can correspond totransitions from one representation (e.g., an image of the mouse, beingalive) to another representation (e.g., of the mouse eating poison). Inthis disclosure, digital content transitioning from one representationto another may be referred to as a state change and/or an evolution. Ina number of embodiments, an evolution may be triggered by the artist, byan event associated with the owner of the artwork, randomly, and/or byan external event.

When NFTs representing digital content are acquired in accordance withsome embodiments of the invention, they may also be associated with thetransfer of corresponding physical artwork, and/or the rights to saidartwork. The first ownership records for NFTs may correspond to when theNFT was minted, at which time its ownership can be assigned to thecontent creator. Additionally, in the case of “lazy” minting, rights maybe directly assigned to a buyer.

In some embodiments, as a piece of digital content evolves, it may alsochange its representation. The change in NFTs may also send a signal toan owner after it has evolved. In doing so, a signal may indicate thatthe owner has the right to acquire the physical content corresponding tothe new state of the digital content. Under an earlier example, buying alive mouse artwork, as an NFT, may also carry the correspondingpainting, and/or the rights to it. A physical embodiment of an artworkthat corresponds to that same NFT may also be able to replace thephysical artwork when the digital content of the NFT evolves. Forexample, should the live mouse artwork NFT change states to a decayingmouse, an exchange may be performed of the corresponding painting for apainting of a decaying mouse.

The validity of one of the elements, such as the physical element, canbe governed by conditions related to an item with which it isassociated. For example, a physical painting may have a digitalauthenticity value that attests to the identity of the content creatorassociated with the physical painting.

An example of a physical element 1690 corresponding to an NFT, inaccordance with some embodiments of the invention is illustrated in FIG.16 . A physical element 1690 may be a physical artwork including, butnot limited to, a drawing, a statue, and/or another physicalrepresentation of art. In a number of embodiments, physicalrepresentations of the content (which may correspond to a series ofpaintings) may each be embedded with a digital authenticity value (or avalidator value) value. In accordance with many embodiments of theinvention, a digital authenticity value (DAV) 1680 may be therefore beassociated with a physical element 1690 and a digital element. A digitalauthenticity value may be a value that includes an identifier and adigital signature on the identifier. In some embodiments the identifiermay specify information related to the creation of the content. Thisinformation may include the name of the artist, the identifier 1630 ofthe digital element corresponding to the physical content, a serialnumber, information such as when it was created, and/or a reference to adatabase in which sales data for the content is maintained. A digitalsignature element affirming the physical element may be made by thecontent creator and/or by an authority associating the content with thecontent creator.

In some embodiments, the digital authenticity value 1680 of the physicalelement 1690 can be expressed using a visible representation. Thevisible representation may be an optional physical interface 1670 takenfrom a group including, but not limited to, a barcode and a quickresponse (QR) code encoding the digital authenticity value. In someembodiments, the encoded value may also be represented in anauthenticity database. Moreover, the physical interface 1670 may bephysically associated with the physical element. One example of such maybe a QR tag being glued to or printed on the back of a canvas. In someembodiments of the invention, the physical interface 1670 may bepossible to physically disassociate from the physical item it isattached to. However, if a DAV 1680 is used to express authenticity oftwo or more physical items, the authenticity database may detect andblock a new entry during the registration of the second of the twophysical items. For example, if a very believable forgery is made of apainting the forged painting may not be considered authentic without theQR code associated with the digital element.

In a number of embodiments, the verification of the validity of aphysical item, such as a piece of artwork, may be determined by scanningthe DAV. In some embodiments, scanning the DAV may be used to determinewhether ownership has already been assigned. Using techniques like this,each physical item can be associated with a control that preventsforgeries to be registered as legitimate, and therefore, makes them notvalid. In the context of a content creator receiving a physical elementfrom an owner, the content creator can deregister the physical element1690 by causing its representation to be erased from the authenticitydatabase used to track ownership. Alternatively, in the case of animmutable blockchain record, the ownership blockchain may be appendedwith new information. Additionally, in instances where the owner returnsa physical element, such as a painting, to a content creator in orderfor the content creator to replace it with an “evolved” version, theowner may be required to transfer the ownership of the initial physicalelement to the content creator, and/or place the physical element in astage of being evolved.

An example of a process for connecting an NFT digital element tophysical content in accordance with some embodiments of the invention isillustrated in FIG. 17 . Process 1700 may obtain (1710) an NFT and aphysical representation of the NFT in connection with an NFTtransaction. Under the earlier example, this may be a painting of aliving mouse and an NFT of a living mouse. By virtue of establishingownership of the NFT, the process 1700 may associate (1720) an NFTidentifier with a status representation of the NFT. The NFT identifiermay specify attributes including, but not limited to, the creator of themouse painting and NFT (“Artist”), the blockchain the NFT is on(“NFT-Chain”), and an identifying value for the digital element (“no.0001”). Meanwhile, the status representation may clarify the presentstate of the NFT (“alive mouse”). Process 1700 may also embed (1730) aDAV physical interface into the physical representation of the NFT. In anumber of embodiments of the invention, this may be done by implanting aQR code into the back of the mouse painting. In affirming the connectionbetween the NFT and painting, Process 1700 can associate (1740) theNFT's DAV with the physical representation of the NFT in a database. Insome embodiments, the association can be performed through making noteof the transaction and clarifying that it encapsulates both the mousepainting and the mouse NFT.

While specific processes are described above with reference to FIGS.15-17 , NFTs can be implemented in any of a number of different ways toenable as appropriate to the requirements of specific applications inaccordance with various embodiments of the invention. Additionally, thespecific manner in which NFTs can be utilized within NFT platforms inaccordance with various embodiments of the invention is largelydependent upon the requirements of a given application.

Blockchain-Based NFT Platforms Incorporating Recommendation Platforms

A variety of systems that incorporate blockchain-based recommendations,including rich media systems, are described above. In severalembodiments, blockchain-based NFT platforms are provided thatincorporate one or more recommendation platforms that identify likelyalignment of a product and/or service with one or more users. In anumber of embodiments, the recommendation platform determines alignmentbased upon on one or more factors indicative of (but not limited to) theexpected enjoyment and/or the expected utility value of the product orservice. In several embodiments, the recommendation platform is capableof generating recommendations that are not specific to particular targetusers. In several embodiments, the recommendation platform can improvean initial user-independent recommendation by targeting therecommendation to the one or more intended users. In variousembodiments, the recommendation platform generates recommendationsand/or improves recommendations based upon user information such as (butnot limited to) demographic information and/or prior usage informationof other products and services related to the one or more users. Incertain embodiments, the recommendation platform can use economicindicators such as inflation levels and consumer sentiment.

Recommendation platforms in accordance with various embodiments of theinvention take as input one or more configuration parameters obtainedfrom or about the one or more users associated with the buyer of theproduct or service, where these configuration parameters may either beexplicitly stated (e.g., age, zip code and interests); implicitlyderived (e.g., from a series of past purchases) or a combination thereof(e.g., a series of assessment by the one or more users, on one or moreprevious purchases). In several embodiments, the recommendation systemalso takes risk factors into consideration when generating one or morerecommendations. For example, in the context of investing, severalsmaller and diversified investments are commonly less risky than onebigger investment. As for the smaller investments, it is less likelythat they all fail to appreciate in value. On the other hand, apurchaser with higher risk appetite or more long-term investment plansmay be able to benefit for a smaller number of large investments,assuming these maintain a higher profile and therefore benefit fromgreater appreciation. As can readily be appreciated, the specificinformation utilized by recommendation platforms in the generation ofrecommendations is largely dependent upon the requirements of specificapplications and/or the characteristics of particular users.Blockchain-based NFT platforms that incorporate recommendation platformsin accordance with various embodiments of the invention are discussedfurther below.

Recommendation Platforms

A process for generating a recommendation using a recommendationplatform in accordance with an embodiment of the invention isillustrated in FIG. 18 . The process 1800 includes determining (1801) aneed for a select user. In several embodiments, the need can bedetermined based upon receipt of a search query. A need can also bedetermined in response to observing a user action, such as (but notlimited to) a purchase, the rendering of or click on a webpage, aninteraction with a URL embedded in an email or an SMS, the installationof or opening of an app, or more. An example need may correspond to aninterest in a service or a product type.

A set of weights can be determined (1802) for the user and relative toone or more sources. A source may be another user with whom a comparisonof interests are made, and a weight can be computed to reflect thecorrespondence between the user and the source. The source may also be aprovider of recommendations as discussed further below.

In several embodiments, a weight associated with the user and a sourcesuch as a provider of recommendations indicates the likely agreement ofthe views of the user and the recommendation provider, based on pastactions of the user and of the recommendation provider. Example actionsinclude (but are not limited to) purchases, requests to viewinformation, recommendations (including reviews) created by the user orrecommendation provider, actions taken by the user in response torecommendations and a comparison between these recommendations andrecommendations associated with the recommendation provider. The weightsmay be an assessment of similarity between the user and the source,whether for the identified need (1801) or more generally. As can readilybe appreciated, the specific manner in which weights are generatedand/or associations are identified between particular users is largelydependent on the requirements of a given application.

The computed weights can be applied (1803) to one or more actionsassociated with the source, for actions such as (but not limited to)those considered during the evaluation (1802) of the weights and relatedto the need (1801). By applying the weights to the actions, actions ofhighly similar sources are given a greater importance than actions ofless similar sources, wherein the similarity is relative to the user.For example, if the need is an interest to buy an NFT image of dogs, andthe source has similar dog-related interests as the user, then anyrecommendation or other indication of interest associated with thesource and relative to the need are used as an indication of potentialvalue to the user.

In a number of embodiments, an assessment is made as to whethersufficient information is available to make a reliable recommendation.In several embodiments, a precision is determined (1804) and compared toa threshold to determine whether the precision is sufficient to generatea recommendation. If only a small number of sources have been identifiedas relevant, or the sources were not highly similar to the user, thenthe precision may not be sufficient, based on a threshold set by theuser or for the user by the system. If so, a new source is selected(1806), if available, and the new source is utilized to determineweights (1802). If the precision is insufficient but there are no morerelevant sources, then the current (but low-precision) recommendation isgenerated (1807). If the precision is determined (1804) to besufficient, a high reliability recommendation is generated (1805).

In a number of embodiments, irrespective of the precision, therecommendation of a product or service takes the form of a ranking ofavailable alternatives. In many embodiments, the recommendation is to asingle alternative. In certain embodiments, the recommendation caninclude an advertisement and/or promotion. As can readily be appreciatedthe specific recommendation(s), advertisement(s) and/or promotion(s)presented are largely dependent upon the requirements of a particularapplication and/or can be configured by individual users.

The generation (1805, 1807) of a recommendation can include generating arendering of information to a user via a user interface. In manyembodiments, the generation of a recommendation can also automaticallyinitiate an attempt to initiate a transaction with respect to animmutable ledger. For example, a transaction may be initiated based uponthe bytecode of a smart contract associated with a user account and amatching smart contract associated with a provider of merchandise and/orservices. In several embodiments, the specific transaction that isinitiated can correspond to a ranking of matching resources.

While specific processes are described above with reference to FIG. 18concerning the generation of recommendations by a recommendationplatform, any of a variety of processes can be utilized to generationrecommendations including (but not limited to) processes that performcollaborative filtering in which individual users provide informationthat is utilized in the generation of recommendations, processes thatutilize alternative methods for assessing the reliability ofrecommendations, and/or processes that generate recommendationsincluding a variety of organic recommendations and/or targetedadvertisements as appropriate to the requirements of specificapplications. Furthermore, recommendation processes in accordance withvarious embodiments of the invention can be utilized within any of theblockchain-based NFT platforms and/or rich media platforms describedabove as appropriate to the requirements of specific applications.Specific processes for generating recommendations based upon weightsdetermined based upon the actions of other users in accordance withvarious embodiments of the invention are discussed further below.

A process for generating recommendations based upon weights determinedfor a user in accordance with an embodiment of the invention isillustrated in FIG. 19 . As noted above, a matrix of weights can bedetermined (1901) for a user. In several embodiments, one dimension ofthe matrix corresponds to different sources, and another dimensioncorresponds to a weighted array, each entry corresponding to the weightfor one product, computed based on one source and the user, e.g., as isdescribed above with reference to FIG. 18 . A third dimension maycorrespond to different products. Yet other dimensions can representother aspects, such as (but not limited to) temporal aspects, preferenceand/or additional factors.

A matrix of opinions can be obtained (1902), where these opinions relateto various potential needs, as described above with respect to FIG. 18 .A weighted matrix of opinions can also be generated (1903). In a numberof embodiments, the weighted matrix of opinion is generated using matrixmultiplication methods based on the matrix of weights (1901) and thematrix of opinions (1902). As can readily be appreciated, any of avariety of processes can be utilized for weighting opinions asappropriate to the requirements of specific applications. In severalembodiments, the resulting weighted matrix of opinions is stored.

A matrix of precisions can also be computed (1904). In severalembodiments, the matrix of precisions relates to the precision of theelements of the weighted matrix of opinions (1903), and describes theextent to which the opinions apply to the user and are representative ofa sufficiently large sample size and strength of opinion, among otherthings. An opinion with very low precision is largely inactionable,whereas an opinion with high precision can be meaningful to act on as itis likely to be accurate.

In several embodiments, the various matrixes generated during theprocess 1900 are utilized to generate (1905) one or morerecommendations. In a number of embodiments, the recommendations are theopinions for which the precision exceeds a threshold that may either beset by the user or the system, or by both in collaboration. Thedetermined recommendations can be presented to the user, e.g., using aGUI or an API, as discussed further below.

In certain embodiments, advertisers can also pay to present specificrecommendations and/or opinions to users. As can readily be appreciated,advertisers are typically unlikely to spend money to advertiserirrelevant goods and services to users. Accordingly, the willingnessand/or amount of money that an advertiser is willing to spend to presenta recommendation to a user can be indicative of the relevancy of therecommendation. In many embodiments, the extent to which particularadvertisements are subsequently found to be relevant can be a factorthat is utilized in determining whether to present recommendations toprevent advertisers from spamming users with irrelevant advertising. Ascan readily be appreciated, any of a variety of processes can beutilized to supplement recommendations with advertisements and/orpromotions that are likely to be of interest to users as appropriate tothe requirements of specific applications in accordance with variousembodiments of the invention. Processes for determining recommendationsto present to a specific user are discussed further below.

A process for determining a recommendation based both on the likelypreferences of one or more users, and based on the likely price trendsfor the products or services in accordance with an embodiment of theinvention is illustrated in FIG. 20 . The process 2000 includesdetermining (2001) recommendations for one or more users. In severalembodiments, the recommendations can be generated using any of thevarious processes described above with reference to FIG. 19 . As canreadily be appreciated, the specific process that is utilized togenerate a recommendation is largely dependent upon the requirements ofa specific application.

An estimate of scarcity can be determined (2002). In many embodiments,scarcity is inherent to the specific good and/or service to which arecommendation relates. For example, an NFT can be unique or one of alimited edition of NFTs. In several embodiments, the recommendationrelates to a good and/or service that does not have a declared scarcityand a process is utilized to determine and/or estimate scarcity. Avariety of processes for determining scarcity are discussed below. Ascan readily be appreciated, any of a variety of processes fordetermining scarcity can be utilized as appropriate to the requirementsof specific applications in accordance with various embodiments of theinvention.

In many embodiments, a price estimate is determined (2003). In severalembodiments, the price estimate can be determined based on factorsincluding (but not limited to) current prices, historical prices and/orestimated scarcity and appreciation data. Scarcity data can bedetermined as part of the process (2000). In certain embodiments,determination (2001) of a recommendation can involve generatingappreciation data. As can readily be appreciated, generation of anestimated price is an optional aspect of process 2000 and the specificfactors that are utilized in the generation of an estimated price arelargely dependent upon the requirements of specific applications and/orusers.

In order to assist users in evaluating the usefulness of recommendationsand/or estimated pricing information, one or more precision estimatescan be determined (2004). In several embodiments, the determined (2004)estimates indicate the accuracy of provided price estimate(s). In anumber of embodiments, confidence information is provided including (butnot limited to) a variance value, a standard deviation value, a countindicating the number of datapoints the price estimate is based on,and/or another statistical measure of accuracy.

In a several embodiments, the recommendation is output, e.g., using aGUI or an API. In certain embodiments, the recommendation is output incombination with the price estimate and/or the precision estimate. In anumber of embodiments, price estimate and/or the precision estimate isoutput in response to a user request with respect to a specificrecommendation. In certain embodiments, the recommendation that isoutput is accompanied by information in a manner that is specific toadditional data including (but not limited to) at least one estimatedtrend data and/or the user profile of at least one user. As can readilybe appreciated, the specific manner in which a recommendation is outputand the companion information that is provided with the generatedrecommendation is largely dependent upon the requirements of specificapplications.

While specific processes for generating recommendations and/oradditional information that enables evaluation of the reliability ofgenerated recommendations are described above with reference to FIGS.18-20 , any of a variety of processes can be utilized to generaterecommendations and/or reliability information as appropriate to therequirements of specific applications in accordance with variousembodiments of the invention. Furthermore, processes similar to thosedescribed above with reference to FIGS. 18-20 can be utilized within anyof the blockchain-based NFT platforms and/or rich media platformsdescribed above with reference to FIGS. 1-17 . Accordingly,recommendation platforms described herein should be understood as notlimited to any specific recommendation process and/or to use within anyspecific blockchain-based NFT platform architecture.

Recommendations Based Upon User Profiles

Recommendation platforms in accordance with many embodiments of theinvention utilize user profiles in the generation of recommendations. Ina number of embodiments, the recommendation platform takes as input auser profile for a user i. In certain embodiments, the user profile caninclude at least some demographic data, some action data, and in manycases also some recommendations associated with user i. Here, thedemographics may at least in part be generated from action data, but mayalso be obtained from the user, e.g., age, gender and zip code. Actiondata can include purchases and other monetization events such as clickson advertisements, as well as user search terms, browsing data,co-location data indicating potential bonds with other users, and more.Various processes for automatically generating user profiles and thespecific data that can be automatically aggregated within user profilesin accordance with a variety of embodiments of the invention arediscussed further below.

In several embodiments, recommendations generated by a recommendationplatform include opinions collected from user i, including positiveindications such as (but not limited to) positive reviews and repeatpurchases; negative indications such as negative reviews, merchandisereturns, complaints, switches to other competing products; andintermediate indications such as luke-warm reviews. Some of these, suchas reviews, are explicitly generated opinion expressions, whereasothers, such as repeat purchases and merchandise returns are implicitlygenerated opinion expressions. Based on the user profile data of theinput, one or more other user profiles, such as the user profile of auser j, are compared with the user profile data of the input and asimilarity measure is determined. The similarity measure may be generalor may be related to a particular interest area, such as horses, joggingor photography. The general similarity measure S_(ij) between user i andj is a value between 0 and 1, and the topical similarity measure ST_(ij)between user i and j, of topic T, is a value between 0 and 1. In oneexample situation S_(ij)=0.2 and ST_(ij)=0.85 for T=music. That meansthat users i and j are not very similar in general, but their musictastes are very highly aligned. Each similarity measure, such as S_(ij)and ST_(ij), also have a precision value P_(ij) and PT_(ij), where theprecision value indicates how accurate the estimate of the correspondingsimilarity measure is, e.g., based on the number of observations thatthe similarity measure is based on. A large number of observations meansthat the precision is high, which in turn means that the correspondingsimilarity measure is a highly accurate predictor of shared opinions,whether in general for all observed demographics and actions, or for aparticular topic. For example, a precision of P_(ij)=0.75 means that thecorresponding estimate S_(ij) is a very accurate assessment, whereas aprecision of P_(ij)=0.02 means that there is very little certainty inthe estimate S_(ij). Based on the similarity measure and thecorresponding precision, a recommendation can be generated. Therecommendation can include one or more assessments of products andservices, and a likelihood that user i would find these desirable, basedon the observed actions and based on opinions of user j, whetherimplicitly or explicitly generated. Identification of a user with a highsimilarity measure and a high precision value can be valuable todetermine likely preferences for a given input user, whereas a user withlow similarity or low precision is likely to be less valuable, if atall. If a malicious user provides fake recommendations, e.g., based onbeing paid to write reviews, the odds that there is a high similaritybetween that user and a given input user is low, as the profile(including the fake recommendations and the absence of associatedbehavior) of the malicious user are unlikely to correspond to theprofile of the input user. Therefore, malicious users will not be ableto influence the disclosed system as much as they can in today'srecommendation systems.

In several embodiments, the recommendation platform takes as input auser profile and at least some demographic data related to the availablelevel of disposable income. An individual user's disposable income maybe estimated in many ways, including one or more of a review of previouspurchases, blockchain-based transactions, their location, such as theirzip code, their background such as employment or tax history, or credithistories—many of which are publicly available on the Internet. Forexample, Alice has a history of purchases that correlates to itemsassociated with other users with high levels of disposable income. Alicealso has a history of purchasing songs, movies, artwork, and luxuryproducts such as a genuine Gucci handbag and expensive dinnerware thatthe system has determined to be luxury goods. The system scores Alicewith a disposable income score of 0.85 where a score approaching 0.1 mayindicate minimal disposable income, 0.5 may indicate average income and0.95 may indicate an ability to purchase without concern for finances orbudget. In certain embodiments, the recommendation platform can alsodetermine the item's luxury score with a music download having, perhaps,a score of 0.4 and a kitchen plasticware container having a luxury scoreof 0.2, and an investment NFT having a luxury score of 0.91. In variousembodiments, a machine learning algorithm determines a given item'sluxury score and projects trends based upon the buyer demographics. Forexample, an item with a modest luxury score of 0.7, such as an expensivenew game software, may be trending among users with disposable incomescores below 0.5 indicating a trend toward unusually high demand amongthose that are typically too budget-constrained to make such a purchase.As can be readily appreciated, the specific information aggregated abouta user in a user profile and/or the mechanisms utilized to infer userprofile information are largely dependent upon the requirements ofspecific applications and/or the information sources available to therecommendation platform. As noted above, systems such as (but notlimited to) rich media platforms can enable recommendation platforms topay users to access user profile data aggregated concerning users via apermissioned blockchain. Specific processes for generating user profilesin accordance with various embodiments of the invention are discussedfurther below.

A process for generating a user profile in accordance with an embodimentof the invention is conceptually illustrated in FIG. 21 . The process2100 includes generating a user profile (2104) from implicitconfiguration parameters (2101) associated with a user; explicitconfiguration parameters (2102) associated with the user, and userobjective data (2103). Example implicit configuration parameters (2101)include (but are not limited to) observations of actions associated withthe user, where actions may include searches, clicks, ad conversions,purchases associated with the user. Example explicit configurationparameters (2102) include (but are not limited to) ratings generated bythe user, recommendations generated by the user, and/or responses tosurveys by the user. Example user objective data (2103) may include (butis not limited to) search terms, indications of the extent to which thegoal is for the service or product to maintain or increase in value, andmore. The user profile (2104) can include (but is not limited to) thesimple combinations of inputs 2101, 2102 and 2103, such as aconcatenation; it may also involve the generation of one or morepredicates describing the user, where said predicates are generated frominputs 2101, 2102 and 2103. Example predicates generation is disclosedin U.S. Pat. No. 10,951,435, titled “Methods and apparatus fordetermining preferences and events and generating associated outreachtherefrom”, which is incorporated by reference in its entirety. As canreadily be appreciated, the specific sources of information utilized inthe generation of a user profile are largely dependent upon therequirements of particular applications.

As discussed above, the extent to which specific user's opinions arerelevant to the generation of a recommendation can be utilized inweighting the extent to which a specific user's actions contribution tothe resulting recommendation. A process for determining weights basedupon a user profile in accordance with an embodiment of the invention isconceptually illustrated in FIG. 22 . In the illustrated process 2200,demographic data for a first user (2201) is obtained. Some of this datamay be implicit, such as gender estimates based on clicks and purchases,whereas other data may be explicit, such as shipping addresses. Actiondata for the first user (2202) can also be obtained, where the obtainedaction data may overlap with demographic data for the first user (2201),but does not have to. For example, action data for the first user mayinclude (but is not limited to) clicks, scrolls and relative renderingtimes indicating the time a user spent on a particular webpage; recordedpurchases; and/or the contents of received recommendations coming fromthe first user. Some of this may be explicit, such as the recordedpurchases, while other may be implicit, such as estimates of interestlevels based on the time a user spends on a webpage or using a givenapp. Systems and processes that can be utilized in the gathering of suchaction data in accordance with various embodiments of the invention arediscussed further below.

From demographic data for the first user (2201) and action data for thefirst user (2202), a user profile (2203) can be generated by the system.Variant approaches for generating the user profile (2203) can be used,as will be appreciated by skilled artisans. Based on the user profile(2203) for the first user and additional user profiles, at least onesimilarity measure and one precision value is generated (2204) andstored. Based on at least one weight and one precision value (2204), atleast one weight (2205) can be generated. In several embodiments, theweight describes how to use data of the additional users to determinerecommendations for the first user. Various examples of processes fordetermining recommendations that can utilize weights generated in thismanner are described above with references to FIGS. 18-20 . Furthermore,the disclosed system can use techniques disclosed in U.S. Pat. No.10,993,082, titled “Methods and apparatus for device location services”,U.S. Pat. No. 10,951,435, titled “Methods and apparatus for determiningpreferences and events and generating associated outreach therefrom”,U.S. Pat. No. 10,936,749, titled “Privacy enhancement using derived datadisclosure”, and U.S. Pat. No. 10,887,447, titled “Configuration andmanagement of smart nodes with limited user interfaces”, all of whichare incorporated by reference.

While specific processes are described above with respect to FIGS. 21and 22 for generation of user profiles and the weighting of thecontributions of specific users to the generation of recommendationsbased upon factors including (but not limited to) user profileinformation, any of a variety of processes can be utilized to generateuser profiles and/or determine weightings to be applied to particularactions of a given user in the generation of a recommendation by arecommendation platform as appropriate to the requirements of specificapplications in accordance with various embodiments of the invention.

Generating Recommendations Based Upon Resource Characteristics

Recommendation platforms in accordance with several embodiments of theinvention provide recommendations with respect to one or more resources,such as physical items, services, or digital items, including NFTs,based upon factors including (but not limited to) their origin, theirformat, and the appreciation (or lack thereof) of the resources andrelated resources (including other resources from the same origin, orother resources liked by the same users). For limited resources, such asNFTs, services that do not scale well and physical items with somelimitation in terms of production quantities, an assessment is made ofthe extent to which the resource will increase in value based onscarcity, resulting in a scarcity score for the resource. This may beachieved using recommendation scores for related resources, e.g., of thesame type or from the same origin, price trends of related items; actualscarcity of related items, etc. For all resources, an assessment is alsomade related to the likely desirability of the resource among users ingeneral. This may be achieved using recommendation scores for relatedresources, e.g., of the same type or from the same origin. This resultsin a desirability score that indicates how much the resource is expectedto be appreciated. Both the scarcity score and the desirability scoreare indicators of likely future value. Items that are scarce are oftenperceived as desirable. Therefore, a correlation between scarcity anddesirability is common. However, many items are desirable and notscarce. Fuel for a vehicle is desirable, yet fuel is a plentifulcommodity. Music is also desirable, yet songs are infinitelydownloadable and inexpensive. Scarcity alone is an insufficient measurefor the machine learning system, otherwise it might recommend one veryugly but unique piece of artwork. Because of the human emotional traitto desire scarce items, the combination of desirability and scarcityscores is a uniquely important aspect of determining future trends.

In many embodiments, recommendation platforms generate recommendationsby assessing a product's feature vector. For example, a particularproduct, whether real or virtual has characteristics, as may theproduct's current or potential owner. These characteristics may include,but not limited to, age, zip code, interest of the user, risk factors,user profile data, reviews from the users, origin, format, resources,appreciation index, etc.

In certain embodiments, the features of a product are expressed as anN-dimensional feature vector. A specific N-dimensional feature vectorcan be created for each item including (but not limited to) an NFT orVirtual Product. The elements of the feature vectors or feature sets mayinclude information that describes the item itself (e.g., an artwork),for example if it is a music file, it uses features that describe tempoand pitch, whereas if it is visual art, it uses features that includecolor, hue and light. A collection of items may be represented in asmaller dimensional space (e.g., a 2 or 3 dimensional space), simplifiedusing dimensionality reduction techniques including (but not limited to)Principal Component Analysis (PCA) of the N-dimensional features sets.In several embodiments, each item, such as (but not limited to) an NFTor Virtual Production, maintains the knowledge of its full N-dimensionalFeature Vector within a collection. In certain embodiments, the featurevector can be encoded within the bytecode of a smart contract recordedon an immutable ledger encoding the NFT or Virtual Production and thefeature vector can be accessed by a recommendation platform. In a numberof embodiments, the feature vector is recorded on a permissionedblockchain that restricts access to feature vectors as appropriate tothe requirements of specific applications.

In certain embodiments, a collection of items (e.g., NFT or VirtualProduction) may be transformed using machine learning or artificialintelligence to self-organize using each item's N-dimensional featurevector to result in a low dimensional (e.g., 2 or 3 dimensional) selforganized map of the collection of items. A skilled artisan willrecognize that variations on these methods can also be used forself-organization including with an artificial neural network usingbackpropagation or Bayesian learner. The same methods and tools mayidentify trends based upon changes to the feature vectors with time. Inseveral embodiments, the system may organize items (e.g., virtualartwork) by genre and use Euclidean distance or K-nearest neighbor torecommend a similar item to a prospective buyer. As can readily beappreciated, the specific manner for expressing the characteristics ofresources and the manner in which that information is accessible to andutilized by recommendation platforms in accordance with variousembodiments of the invention is limited only by the requirements ofparticular applications.

Processes for Generating Pricing Estimates

Recommendation platforms in accordance with a number of embodiments ofthe invention incorporate a valuation predictor that assesses the likelyfuture value of the product based on one or more assumptions or beliefs,such as the continued scarcity of the product or service or a generalincrease or decrease of appreciation of products or services of thecategory over the time of the prediction. These two components,together, strengthen each other by providing assessments of likelyenjoyment, value and valuation, as well as other parameters describedherein. One application of the use of valuation predictors is theidentification of NFTs and/or other virtual products that are good fitsfor a potential acquirer, whether the acquisition is of exclusive ornon-exclusive access rights, and whether the assessment is primarily forpurposes of individual (or purchasing entity-based) use; for purposes ofinvestment; or a combination thereof.

The scarcity of an item, whether real or digital can be computed basedupon supply and demand. For example, the market price of acryptocurrency is generally based upon the supply of freely availablecoins, which may be the total number of coins in existence less thosethat are held tightly for investment purposes, and the market demand forthe coins. If there are few coins freely available for trade, but thereis a lot of demand, then the item may be considered scarce and the priceis likely to rise. In one embodiment, the system may perceive thescarcity to be the rate of change in availability, such as one mightfind in the lodging industry. As an illustrative but non-limitingexample, when hotels are full, scarcity is scored high, say at a levelof 1. When hotels are half-full, the level is 0.5. When the systemdetects construction of an unopened hotel, or the advent of a newcompeting service such as home rentals, the scarcity score is likely todrop. Demand for an item is often a result of the item's desirability.

An item's desirability may have a dramatic influence on demand andpricing. In one embodiment, the system may perceive a value ofdesirability based upon the volume of trades during a period versus thetotal number of items in existence. For example, an artist may havecreated 100 works of glass-blowing art during his career. If, in anormal year, only 5 of those trade hands, but the past year has seen 20trade hands, maybe because of the death of the artist, the desirabilityscore will increase. In this example, a score may be computed from thenumber of annual trades divided by the number of works of art which, forthe past year would result in a value of 0.2, significantly higher thanthe 0.05 typical of prior years. In several embodiments, the system mayperceive a desirability score based upon price trends for an item, oreven a combination of exchange volumes and pricing changes. If the sameartwork is selling for 10 times the price as compared to prior years,the system may determine the desirability score has increaseddramatically. In this instance, the system might compute a desirabilityscore from the number of annual trades divided by the number of works ofart times the ratio of the price from the past year versus prior years.

The scarcity score and the desirability score, combined, can be used todetermine a likely assessment of an item. For example, a digital artobject in the form of an NFT with a very high scarcity score (such as0.9 out of 1) and a rather high desirability score (such as 0.65 outof 1) will likely appreciate in value more than another item, such as aHonda Civic, with lower scarcity score (such as 0.21) and lowerdesirability score (such as 0.19), assuming they are both pricedappropriately. Therefore, it is also beneficial for the system todetermine what the appropriate price of an item is, which is done basedon the sales of related items, the prices of related items, and theattention paid to such items. For example, an item with a lot of viewsbut no purchase is likely to be priced too high, whereas an item thatwas bought very rapidly is less likely to be priced too high. A relateditem may mean an item from the same originator, such as the same artist,or of the same genre, such as a photo of a cat, but may also bedetermined based on the preferences of users likely to appreciate theitem based on their past history, and the likely actions of such users.For example, some users may be more likely to purchase items, whetherbased on having stronger preferences, more disposable funds, or beingmore excitable. The system determines the appropriate price of an itemand then observes the price that the seller sets, as well as whatactions (e.g., searches, views, placing in shopping basket, completepurchase) that were associated with the item. By comparing the estimatedappropriate price and the set price in light of the actions, the systemcan fine-tune the weights used to determine the appropriate price, aswell as the weights for the desirability score computation. Similarly,weights used for the computation of the scarcity scores are modifiedbased on demonstrated scarcity of items that are related. One score mayindicate the scarcity of items from the same originator (such asartist), and another may indicate the scarcity of items of the samegenre (e.g., puppy videos). Some scarcity scores, such as items of agenre, may be indicative of the desirability score as low sustainedscarcity in combination with solid sales are indicative of a highdesirability, just as high sustained scarcity in combination with risingprices is. Similarly, a low sustained scarcity is likely to indicatethat prices may not be increasing in the near term.

In several embodiments, the scarcity score, the desirability score, thesimilarity score and the precision score are all computed using machinelearning (ML) techniques, using the described features above as inputs,such features including but not being limited to: demographic data,action data, opinion data, appropriate prices and actual prices, anddata related to temporal aspects of such features (such as how rapidlyactions take place). These ML models are used to predict theabove-listed scores, and are improved by turning the weights asobservations are made and compared to the predictions based on thescores.

A process for generating price estimates in accordance with anembodiment of the invention is conceptually illustrated in FIG. 23 . Inseveral embodiments, a recommendation platform can perform the process2300 by obtaining data including (but not limited to origin data (2301),format data (2302) and appreciation data (2303). In many embodiments,the recommendation platform also generates a scarcity score and/or adesirability score (2304).

Example origin data for a first item may be (but is not limited to) thatthe first item, such as a song, may have been produced by artist A, whoreleases one record every year on average. Example format data (2302)can include information that indicates that the first example item is asound file, which can be bought by any number of users from a variety ofmarketplaces for music. In certain embodiments, the format data may alsoinclude additional metadata such as (but not limited to) an indicationof the genre of the song, or more generally, of the items produced byartist A. Example appreciation data includes data indicating thepopularity of the indicated genre, and, if known, the popularity of thefirst song, as indicated by data related to sales and accesses. A secondexample item may have origin data (2301) that indicates that the item isproduced by artist B, who releases two products every three months. Theassociated format data (2302) can indicate that the second item is a jpgimage file available for purchase as an NFT, and that there are 100numbered NFTs available that each enable the purchase of the jpg imagefile. The format data may also specify the size of the jpg image file,the color palette of the image contained within the image file, and whatapplications are allowed to render it, if such a limitation exists. Theappreciation data (2303) of the second item may indicate the transactionhistory for previous sales created by artist B, e.g., how much the itemswere sold for, whether any have been resold, how long items were on themarket, whether there were competing bids, and more. Appreciation data(2303) may also specify similar data related to items from other origins(e.g., other artists) that are deemed closely related, whether by ahuman expert, an automated rule-based expert system or by an ML-basedclassifier.

In certain embodiments, the scarcity score and desirability score (2304)are generated from the inputs (2301), (2302) and (2303). In severalembodiments, the scarcity score and desirability score (2304) may use arule-based algorithm, an ML based algorithm, or a combination thereof.The scarcity score for the first example item may indicate no scarcityas any number of items can be purchased at the same time, while thedesirability score may be 38 out of 100 in general and 62 out of 100 forusers who enjoy rock music. The scarcity score for the second item maybe 34.2 out of 100, and may include a precision value, such as 2.67; thescarcity score may increase as the items sell and based upon the salesvelocity; the associated desirability score may be 17 out of 100.

In many embodiments, price estimate (2307) is computed. The computationof a price estimate can use (but is not limited to) at least one of thescarcity score and the desirability score (2304), timeframe data (2305),trend data (2306), and may also use as input an asking price (notshown). In several embodiments, timeframe data (2305) can indicate forwhat time period a price estimate is to be computed, e.g., “right now,”“in three months”, “for the next 24 months”, and so on. In certainembodiments, the trend data (2306) indicates price trends, appreciationdata trends, scarcity trends, etc., for other items in general; forother items of the same genre; for other items by the same origin,and/or for other items with related format data. In several embodiments,recommendation platforms compute price estimates using one or more or acombination of statistical methods, ML methods, and methods similar to(but not limited to) the approaches described above with reference toFIG. 20 .

While specific processes are described above for generating priceestimates are described above with reference to FIG. 23 , any of avariety of processes can be utilized to generate price estimates and/ordata accompanying price estimates indicating the precision and/or theconfidence of price estimates as appropriate to the requirements ofspecific applications in accordance with various embodiments of theinvention. Accordingly, processes for generating price estimates shouldbe understood as not limited to specific types of input data and/or thevarious inputs described above can be utilized alone, in differentcombinations and/or in combination with additional sources of input dataas appropriate to the requirements of a given application in accordancewith certain embodiments of the invention. Furthermore, the variousprocesses described above for generating price estimates can be utilizedin any of the various recommendation platforms described hereinincluding (but not limited to) those described above with reference toFIGS. 18-20 and the discussion of the analysis of item feature vectors.

As can readily be appreciated, the ability to estimate price can beuseful in generating recommendations. Information concerning goodsand/or services that are likely to become scarce and/or appreciate invalue can also be highly useful. Recommendation platforms that providetrends spotters with the ability to influence recommendations inaccordance with various embodiments of the invention are discussedfurther below.

Trend Spotters

In a number of embodiments, an entity that can be referred to as a trendspotter can make a prediction of one or more of the parameters discussedabove, such as the scarcity score, the desirability score, thesimilarity score and/or the precision score, or related to the pricepoint of one or more resources at a specified time in the future. Thetrend spotter may be a human expert, or software, or a combinationthereof. It is different from the ML-based methods disclosed above inthat it is at least in part externally hosted from the recommendationplatform, and that the exact approach for determining a prediction maynot be known to the recommendation platform. The trend spotter can beassociated with an identity, and the identity can be associated with areputation that indicates the quality of past predictions. This qualitymeasure can be used to assign a weight value to a given prediction. Thetrend spotter may also set precision values related to the prediction ofone or more of the parameters; this precision value can also be used, incombination with the quality value, to determine the weight.

In several embodiments, the reputation score of the trend spotter isdetermined based on this accuracy of its predictions, and how wellothers also predicted a trend. If two trend spotters are equallyaccurate when it comes to determining a future value of a parameter, butone of the trend spotters is far off from other predictions (whethertrend spotters or automated prediction methods such as those disclosedherein) then the one that is far off—but still accurate—is morevaluable, and the associated increase of reputation of this trendspotter is greater. Thus, the reputation value can describe both theaccuracy of the prediction (including both the quality and theprecision) and the extent to which the trend spotter was an outlieramong trend spotters and automated prediction methods.

In certain embodiments, trend spotters can be rewarded in a variety ofmanners, including analogously to how bounty hunters are awarded fortheir efforts, as disclosed in U.S. patent application Ser. No.17/806,065 “Systems and Methods for Maintenance of NFT Assets”, whichclaims priority to U.S. Provisional Patent Application No. 63/208,366titled “Perpetual NFT Assets”, the disclosures of which are incorporatedin their entireties by reference.

In many embodiments, an entity that can be referred to as an influencermakes a recommendation of one or more, but not limited to, assets,products, services, or artists. The influencer can act to influencetheir followers, instigate a trend, and, indirectly, the desirabilityscore of the item. Influencers are traditionally consumers; however, itis anticipated that computer algorithms will mimic the actions ofinfluencers in the future. Whether human, algorithmic, or a combinationof both the intent is to influence purchases and, or, investments.Similar to the trend spotter, it is different from the ML-based methodsdisclosed above in that it is at least in part externally sourced, andthat the exact approach for determining an item to promote may not beknown to the system.

In a number of embodiments, the influencer is associated with anidentity, and the identity is associated with a reputation thatindicates the success of prior influence campaigns. This quality measureis used to assign a weight value to a given influence. If twoinfluencers are equally successful when it comes to influencing a futurevalue of a parameter, but one of the influencers is promoting acompeting item, then their influences may partially offset. Detecting,tracking, and managing the external influencers is disclosed in theco-pending U.S. Provisional Patent Application Ser. No. 63/066,087entitled “Security Enhancements using Atomic State Change Management”,the disclosure of which is hereby incorporated by reference in itsentirety.

While specific mechanisms for implementing automated trend spottersand/or influencers are described above and for generatingrecommendations based upon information sourced from trend spottersand/or influencers, recommendation platforms in accordance with variousembodiments can rely upon any of a variety of sources of informationincluding information provided by users and/or remote systems, where theorigin of the information may be unknown but the reliability of theinformation can be estimated, as appropriate to the requirements ofspecific applications. Additional sources of information that can reliedupon by recommendation platforms are discussed further below.

Betting-Based Recommendation Platforms

Recommendation platforms in accordance with a number of embodiments ofthe invention can utilize a betting-based recommendation process togenerate recommendations. For example, a recommendation platform cancollect recommendations such as “This is a great quality product”, andit can also be used to collect predictions such as “This is a productthat will be in greater demand in three months” or “This is a servicethat is becoming harder to get due to scarcity”.

In a number of embodiments, an ML component receives bets as input andgenerates an output indicating the likely valuation for a given timeperiod, and an associated precision that is related to factors including(but not limited to) the size of the marketplace, the number of betsbeing placed, and/or the average interarrival time of bets, where a lowinterarrival time means an active exchange whereas a high interarrivaltime means a less active exchange. A less active exchange is indicativeof a polarization of opinions. The track record of the providers of betscan be of value when determining the meaning of a polarized marketplacein terms of estimated value and precision, and can be determined usingstandard ML mechanisms trained on past observations.

In several embodiments, recommendation platforms may take the estimatedvalue and the precision as input parameters, along with other parametersas described above, and generate assessments including (but not limitedto) assessments of value, risk and/or more. Parameters can also becomputed using algebraic methods from bets. One simple way is a votingbased approach where the funds bet for one opinion represent a number ofvotes for yes, the number corresponding to the funds value; while thefunds bet for the other and diametric opinion correspond to no votes,the number of which correspond to the amounts invested. This may be awindowed approach in which old investments are given a lower weight thannewer investments. The resulting votes can indicate whether themarketplace believes an event will occur or not. In several embodiments,the price trend curve for the bets is used to indicate changes ofpopular opinion over time, and this change can be used to determineweights used for weighing of other inputs, which may be obtainedexternally, such as (but not limited to) from a traditionalrecommendation system where users indicate whether a product should berated using one, two, three, four or five stars. Here, the number ofstars is the recommendation, and this recommendation may be weighedbased on the trend curve, so that low recommendations with a fallingtrend curve for bets are further reduced whereas high recommendationswith an increasing trend curve are further increased. A skilled artisanwould recognize that there are many variants of this approach, and wouldsee the benefit of using the trend curves for improving the assessmentsof parameters as disclosed herein. Similarly, trends for other types ofuser-provided actions may be used to generate weights for otherquantities, or as inputs to ML algorithms that in part perform suchweighting.

By way of further example, a first user may be interested ininvestment-grade NFTs that are expected to increase in value within oneyear, and where the investment is relatively low-risk. The low risk maybe supported by high quality predictions and high-precision predictions,or a large number of medium-quality or medium-precision predictions thatare supporting the value increase; and the relative absence ofindications of a likely price decline where these indications alsocorrespond to high quality predictions or high-precision predictions.The user may be a human user who uses an interface to indicatepreferences related to risk tolerance, appreciation objectives, anddetailed indicators such as the identities of preferred trend spottersor ML methods used for making predictions. The user may also be analgorithm that is run by an investment company, where the algorithmobtains parameters related to value predictions, such as the parametersdiscussed above, as well as predictions from ML engines and trendspotters, and/or where the algorithm combines these using potentiallyproprietary methods to make assessments of what investments are mostfavorable. The user, whether a human or an algorithm, may then obtaininformation relating to NFTs (or more generally, resources) with thehighest investment potential according to the configurations andcomputations, and make one or more bids or purchases. The greater thevolume of use in this way, the more stable the predictions will be,which is beneficial for investors with low tolerance for risk.

Whereas bids, as will be described herein, provide a link between theexpression of an opinion and a financial opportunity/risk, many of theprocesses utilized by recommendation platforms implemented in accordancewith various embodiments of the invention also apply to endorsements,which provide a link between an expression of an opinion and areputation opportunity/risk, where the reputation is associated with anidentity or pseudonym of the provider of the opinion. Thus, influencersmay affect recommendations not by risking financial value butreputational value (which may then have financial repercussions). Thus,the mechanisms we disclose to process the bids also apply toendorsements, for these reasons, and as will be apparent to a person ofskill in the art. The system implementation may consider requiringinfluencers and users who have made a public recommendation, such as aproduct review, to either disclose the purchase, the recommendation, orto be prevented from making a bid, or relinquishing a previous bid toprevent abuse. Similar abuses may require prevention such as those byemployees and representatives of a product or those with a financialinterest beyond the bid system. Accordingly, the specific manner inwhich bids and/or endorsements or other actions are relied upon in thegeneration of recommendations is largely dependent upon the requirementsof specific applications.

A process for generating a betting-based recommendation in accordancewith an embodiment of the invention is conceptually illustrated in FIG.24 . In the illustrated embodiment, the recommendation platform receivesa user profile 2408 for a user for whom the recommendation is to begenerated. In addition, the recommendation platform performs a process2400 in which it receives data including (but not limited to) one ormore of betting-based recommendation data (2401), review-basedrecommendation data (2402), and/or proprietary prediction data (2403).In several embodiments, the proprietary prediction data (2403) may beproduced by an expert, by an algorithm that may not be known to thesystem, or a combination thereof. As can readily be appreciated thespecific information obtained during the process 2400 is largelydependent upon the requirements of a specific application.

In addition, the recommendation platform can obtain an optional searchquery (2404) that indicates the interests of the user. When a searchquery (2404) is not available, the recommendation platform can generatea general recommendation not based on any particular request, orgenerates one or more recommendations based on previous requestsassociated with the user. In addition, the recommendation platform canuse one or more values that are a price estimate (2407) and that may becompared with information related to user profile (2408) to determinewhether the associated product or resource is likely to be of relevanceto the user, e.g., based on past purchases or activities. In severalembodiments, the recommendation platform generates a ranking of items(2405). In a number of embodiments, the generation of the ranking ofitems (2405) may use an ML based algorithm whose weights are fine-tunedin response to information about how the user reacted to the ranking ofitems, e.g., by clicking to get more information on one or more of theitems indicated in the ranking of items, by performing one or morepurchases, searchers, or by accessing any associated resources. Thus,the accuracy of the prediction algorithm that produces the ranking ofitems can be improved in response to observations related to the userand her actions.

While specific processes are described above with reference to FIG. 24for generating recommendation based upon betting-based recommendationdata, any of a variety of processes can be utilized to generaterecommendations based upon information derived from betting informationas appropriate to the requirements of specific applications inaccordance with various embodiments of the invention.

By way of an additional example, a user can invest in NFTs that, inaddition to having reasonable investment potential with respect to sometime frame, also satisfy some personal criteria of the user. Forexample, the user may invest in music-based NFTs and movie scripts forsci-fi movies, but not in JPG-based NFTs or any NFT that does not comefrom a source that is certified to be an equal opportunity employer.Thus, this second user inputs, whether using a GUI for a human user oran API for an algorithm user, a search configuration, obtaining inresponse a collection of potentially ranked resources. The ranking maybe performed using one or more dimensions, such dimensions includingestimated 12-month appreciation, estimated 48-month appreciation, risklevel, and the precision of estimates associated with the precision ofthe underlying inputs to the ML algorithm that determines likelyappreciation. Other example parameters related to which the ranking canbe performed are also possible, as will be appreciated by a skilledartisan. The second user may be provided with suggestions of bids thatare likely to be accepted where there is no fixed price set, and canprovide a bid for one or more of the items. In some instances, the usermay wish to buy a given combination of resources, and only if he or shewill obtain all of them. This is well handled using a smart contractthat specifies the conditions of the purchase. This can be matched to afirst resource and to a second resource at the essentially same time orin serial, and if both match, and the purchase terms are acceptable tothe sellers, then both purchases take place essentially at the sametime, and only when both resources are available.

In a third example, a user is interested in investing in futures inservice provision or merchandise, and may wish to purchase the right tohaving a meal for 12-18 people delivered for Thanksgiving, relative to aparticular geographic area, from a particular vendor. The user thus maypay an agreed amount, such as $450, for this right. The real price ofthe service may be $1800, and the remaining $1350 that are not paidwould be due a week before delivery. This is beneficial for the providerof the resource (in this example, a Thanksgiving meal for 12-18 people)as it enables planning and investment in ingredients, and it is alsobeneficial for the user as it enables planning ahead, and also financialbenefit. Namely, if the user is not interested in taking delivery of theservice, he can resell the contract before the time of delivery. If theprovider is a recognized chef of a restaurant, the user may be able toresell the right to delivery, for which he paid $450, for $900 a monthbefore delivery. The buyer of the contract would then pay $1350 to theservice provider a week before delivery. If a user with the right ofdelivery does not pay the final instalment at the specified time, thecontract would not be binding to the provider of the resource, who couldthen either downscale its effort or sell the contract to another party.Like NFTs, it is possible to predict the value of resources such asthese based on historical data and analysis of providers, scarcity, andmore. Similarly, just as the chef may be the seller of a resource, he orshe can also be the buyer of another resource, such as one or moreingredients. Thus, an agent of the chef may automatically generate acontract for purchasing raw turkey upon the sale of a Thanksgiving meal.This automation simplifies the processing, and also extends the benefitsof planning ahead down the chain, e.g., to farmers and other providersof resources consumed by the check or other service provider. An agenthere is a software program or a software program with some user inputand configuration.

In a number of embodiments, a company can bid up the value of bidsrelated to positive reviews of their products and services, essentiallyholding all such contracts at buoyed prices, thereby hoping toartificially increase the recommendations associated with their productor service. However, recommendation platforms in accordance with manyembodiments of the invention can also consider the price of negativereviews. If these are also high, that is indicative of two groups ofinvestors with diametric views on value, or of a self-interested partysupport-buys the positive opinions. Either will be identified asindicators of risk to the recommendation computation entity, which willidentify high positive prices and low negative prices with a positiverecommendation that can be relied on, and low positive prices and highnegative prices with a negative recommendation that can be relied on.High prices of both these contracts reduces the extent to which theassociated recommendation can be relied on, thereby reducing theassociated weight of the associated recommendation score. Theself-interested party cannot suppress the prices of negative reviews bybuying and holding these contracts, as holding contracts means setting aprice higher than others are willing to pay, and their assumed goal isto set a lower price. Setting the lower price will cause the sale of thecontract, which would then only be resold at a higher price by thepurchaser. A very high turnaround of the negative reviews thereforeindicates subsidized selling followed by increased-cost buying by theself-interested party. Apart from this being very costly to maintain forthe self-interested party, the pattern of high turnaround for negativeopinions and limited turn-around for positive opinions is indicative ofsuch a subsidizing effort of negative opinions, and bloating of pricesof positive opinions. The recommendation computing entity can thereforeidentify patterns such as these and reduce the weight associated withsituations that are indicative of attempts to artificially affect theprices of the contracts.

Recommendation platforms in accordance with a number of embodiments ofthe invention utilize techniques disclosed in “Financial Instruments inRecommendation Mechanisms”, by Markus Jakobsson, published in FC 2002:Financial Cryptography pp 31-43, and hereby incorporated by reference inits entirety. We refer to this as the FIRM recommendation. The FIRMrecommendation technique has benefits but also shortcomings and flaws,and can be extended and improved as disclosed herein, and incorporatedwithin recommendation platforms in multiple ways. For example, in oneinstance, a recommendation platform can be configured to request fromthe bet providers bets related to the value of the related product in 12months, whereas in another it is configured to request the value in 24months. In one instance, a bet provider can choose to place bets bothfor a 12 month outcome and a 24 month outcome. The cost of placing thesebets will be computed based on the marketplace of bets up to the time ofa given bet. A bet provider may be told that one bet relates to thevalue of a resource rising by at least 110% of the current value in 12months, and that the cost of this bet is $0.33 per unit of bet, whereasthe bet relating to the value falling to no more than 90% of the currentvalue in 12 months may cost $0.5 per unit of the bet, indicating thatthe current consensus indicates that a rise in value is more likely thana fall in value. At the same time, the costs for the 24 month predictionmay be $0.1 per unit of the bet for an increase to 120% of the currentvalue in 24 months, and $0.8 for a reduction to below 80% of the currentvalue in the same time period. This indicates that the long-termconsensus is an even stronger growth in value for the resource for thelonger (24 month) term than the shorter (12 month) term. In one otherinstance, the user is asked instead to make bets related to the relativevalue of two products, e.g., a bet that product A will increase more invalue within 12 months than product B will. Here, a product mayrepresent a basket of items, or just one item, where the item may be aphysical good, a virtual good, or a service. Bets can also be made onother properties, such as market fluctuations, precision, the accuracyof a given one or more recommendations relative to another one or morerecommendations, and other variations, as will be understood by askilled artisan.

As time passes, and more insights are gained about the product orservice in question, these prices would change. That means that a betthat cost $0.33 when made may suddenly become worth $0.5, which isindicative of the person making the $0.33 bet was right in the eyes ofthe developing trends. A person who has made a bet in the past will thenbe able to resell the bet to somebody else, like a futures contract isresold. Not just existing bets are used for the recommendationmechanism, but also bets that have been resold in this manner. Based onthe duration of time the user held the bet, a different weight may beassigned to a resold bet, where a bet that was held for a long timewould be given a greater weight than a bet held for a shorter period oftime, but at the same time, a lesser weight than a bet that is currentlyheld. A bet that becomes worth less over time, e.g., goes from $0.5 to$0.21 is indicative of a view that became debunked. A person with thisview, if he still has the view, may want to buy more bets at that lowerprice, or hold on to the existing bet, but may want to sell the bet ifhe or she thinks that the value is going to fall still more,corresponding to a situation in which the user thinks that the productor service will become less and less appreciated onwards. Adecentralized marketplace for recommendation bets that includes optionaluser privacy is disclosed in U.S. Provisional Patent Application No.63/046,556 titled “Privacy Preserving Matchmaking”, which isincorporated in its entirety by reference.

As can readily be appreciated based upon the above discussion, bidsand/or bets provide important signals regarding the potential valuesand/or scarcity of resources. Recommendation platforms in accordancewith many embodiments of the invention can use bids and/or bets alone orin combination with any of a variety of information sources including(but not limited to) those described herein to generate recommendationsas appropriate to requirements of specific applications in accordancewith various embodiments of the invention. Specific techniques forgenerating recommendations using smart contracts encoded on one or moreimmutable ledgers in accordance with various embodiments of theinvention are discussed further below.

Recommendation Platforms Implemented Using Smart Contracts

In the context of blockchain-based NFT platforms, smart contracts can beutilized to implement a betting mechanism similar to any of themechanisms described above. At a first time, there may be neitherpositive nor negative expressions of opinion for a product. These can becreated, pairwise, by a party that is enabled to issue such documents.These can then be sold at market prices by this party. This may operatesimilarly to an Initial Coin Offering (ICO) in which a collection ofsmart contracts are minted by an originator, and then sold to others,the price of each being determined by a marketplace, e.g., initiallysold using an auction. The smart contracts can be written to animmutable ledger and the smart contracts can be transferred between useraccounts.

In several embodiments, the right to generate smart contracts can beconferred on a miner that solves a problem, such as a proof of work orproof of space related problem. Thus, some number of pairs of such smartcontracts can be generated like a coin, and be given value by being soldat market prices, by the miner. In certain embodiments, the miner cansell one but not the other member of a pair, or can sell both. Inseveral embodiments, these prices are public, with the purchasesrecorded on ledgers. As can readily be appreciated, knowledge of theprices, and trends in prices, can enable a recommendation platform togenerate recommendations informed by prices and their trends.

In many embodiments, the coins are smart contracts that are associatedwith a user by the user purchasing one or more smart contracts of thistype. As a user at a second time selects what contracts to buy and atwhat price, this is an implicit recommendation as a user who thinks aproduct is undervalued will buy positive expressions of opinion for theproduct, but not negative. At a third time, the user decides to sell oneor more of these documents. These correspond to the bets describedabove, and can be implemented as smart contracts, which in turn may havea similar structure as coins.

FIG. 25 describes a prior-art coin 2501 including a ledger reference2502, such as an indicator of what ledger element is being closed, or achallenge associated with that element. It also includes proof data2503, which may correspond to a proof of work or a proof of space, wheresuch proof is relative to a challenge associated with the ledger beingreferenced by ledger reference 2502. Proof data 2503 is a function ofledger reference 2502 and public key 2504, and therefore ties these twoelements together. Public key 2504 is used to verify digital signaturesmade using a corresponding private key 2505 (also sometimes referred toas secret key). Such a digital signature (not shown herein) may transfera portion of the value associated with coin 2501 to a party other thanthe holder of private key 2505, e.g., by signing a second public keyassociated with a second private key, where the second private key isknown to the recipient of funds, but preferably not to others.

A coin that can be used to mint smart contracts used to buy and sellrecommendation opinions in accordance with an embodiment of theinvention is conceptually illustrated in FIG. 26 . The coin 2601 can beused to mint documents used to buy and sell recommendation opinions.Coin 2601 includes a ledger reference 2602 similar to ledger reference2502. Coin 2601 also includes a first public key 2604, second public key2605, first descriptor 2606 and second descriptor 2607. Proof data 2603is a function of ledger reference 2602 as well as first public key 2604,second public key 2605, first descriptor 2606 and second descriptor2607, and therefore tie these elements to each other and to the coin2601.

In many embodiments, the first public key 2604 is associated with thefirst private key 2608. In addition, the second public key 2605 isassociated with the second private key 2609, similar to how public key2504 is associated with private key 2505. In a number of embodiments,first descriptor 2606 describes a set of terms such as (but not limitedto) a given number of units of the opinion “Product X will be betterthan average among products in product basket Y in time Z”, and opposingsecond descriptor 2607 describes a set of opposing terms such as (butnot limited to) a given number of units of the opinion “Product X willbe worse than average among products in product basket Y in time Z”. Ascan readily be appreciated, the selection of X, Y and Z can bearbitrary, may be a function of the challenge associated with ledgerreference 2602, and/or be generated on a rotating schedule based on asequence number of the coin 2601.

In certain embodiments, first descriptor 2606 and second descriptor 2607(and associated public and private keys) make up a pair. The coin 2601is shown with one such pair, but may include multiple such pairs. Thefirst public key 2604 can be used to verify digital signatures madeusing the corresponding first private key 2608, and second public key2605 can be used to verify digital signatures made using thecorresponding second private key 2609. Such a digital signature (notshown herein) may transfer a portion of the unit described by the firstdescriptor 2606 (when the first private key 2608 is used) or a portionof the unit described by the second descriptor 2607 (when the secondprivate key 2609 is used). To purchase a bet associated with the termsof the first descriptor 2606, for example, a user would pay an amountbased on the market rate associated with the first descriptor 2606, andin return get a digital signature using the first private key 2608 on amessage that includes a public key associated with the buyer/user, andfor which the buyer/user knows the corresponding private key. As theopinion of products change over time, the market value of these bets,corresponding to first descriptor 2606 and second descriptor 2607, willchange.

In several embodiments, the coin includes, in addition to the componentsshown in FIG. 26 , a public key similar to public key 2504 that can beused to verify the transfer of portions of the coin value, including allof it. By contrast, the first public key 2604 and second public key 2605of the coin are used to verify the transfer of rights associated withfirst descriptor 2606 and/or second descriptor 2607, as described above.This combines the functionality of a prior-art coin, such as describedabove with reference to FIG. 25 , with the bet-management component asdescribed above with reference to FIG. 26 .

A process for selecting items in accordance with an embodiment of theinvention using a smart contract similar to those described above withreference to FIG. 26 in accordance with an embodiment of the inventionis conceptually illustrated in FIG. 27 . Database of items 2701 includesat least two service descriptions. An example service description is“Acme Apple juice tastes better than Aardvark Apple juice” and its mate“Aardvark Apple juice tastes better than Acme Apple juice”. Anotherexample service description is “Acme Apple juice will be better thanaverage among products in product basket of juice products with appletaste in three months starting at the date of creation of the coincontaining this statement” and its mate “Acme Apple juice will not bebetter than average among products in product basket of juice productswith apple taste in three months starting at the date of creation of thecoin containing this statement”.

Each service description in the database of items 2701 can have aposition value, indicating its index, for example. This position valuecan be explicitly stored in the database of items, but does not have tobe in some embodiments. Counter 2702 is a value that indicates aposition in the database of items 2701, corresponding to the next termor set of terms to be used when a coin with a bet-like structure is nextgenerated. The counter may be updated in a round-robin manner, i.e.,increase by two, modulo the length of the database of items 2701, everytime a new counter 2702 is needed. An even number of terms, where thenumber of these is a configuration parameter, are selected for inclusionin a coin, the starting point indicated by the counter. For example,when the counter is 0, which is the beginning of the database, the threefirst pairs may be selected to be included, where the first paid may be“Acme Apple juice tastes better than Aardvark Apple juice” and its mate“Aardvark Apple juice tastes better than Acme Apple juice”. In a numberof embodiments, these correspond to the first descriptor and the seconddescriptor of a coin similar to the coins described above with referenceto FIG. 26 . Alternatively, instead of counter 2702, a need-basedindicator 2703 may be used to determine what pair or pairs to select,e.g., based on what pairs have seen the greatest change in market valuesince a time period corresponding to the minting of the last one hundredcoins associated with the ledger to which the coin to be minted isassociated, where a counter 2702 is used whenever there is a tie to bebroken, for example. This results in an item selection 2704, includingat least one pair of terms to be used for descriptors such as firstdescriptor and second descriptor of a coin similar to the coinsdescribed above with reference to FIG. 26 .

Some items in the database of items may have configurable components,such as a term “Product Acme Apple juice will be X % better than averageamong products in product basket apple juice in time Z” where X and Zare parameter data 2705 selected based on trend data 2706. For example,if trend data 2706 indicates an increase of interest for a product ofX′=12% over a 24 h period, X may be selected as X=2*X′=24%. Similarly,the time Z may be modified based on the need for short-term vs.long-term assessments, e.g., based on volatility in the marketplace.Other types of parameters can be selected including (but not limited to)parameters based on trend data 2706 and other inputs (not shown), aswill be appreciated by a skilled artisan. Since the selection method isdeterministic, all parties, including miners and verifiers, candetermine what the next terms are to be selected for the next coin to beminted. To make this stable over time, any inputs to the algorithm canbe related to a recent but not current point in time, such as an averageof relevant values observed in the previous (e.g., 1 h) time period.This also reduces the extent to which the selection process may bevolatile, and reduces problems related to different market perspectivesdue to limited real-time knowledge among miners and verifiers.

With specific regard to betting-related contract structures, FIG. 29illustrates parameters of a betting-related contract structure. Aspect2900 of an implementation is whether a bet has an end. For example,“Product A will increase its market share by Dec. 31, 2025” is a termwith an end, i.e., a finite 2902 term, whereas “Product A will maintaina market share of more than 25%” does not have an end, i.e., has aninfinite term 2901. In many embodiments, bets without an end will remainrelevant as time passes.

A first bet that is made ahead of a second bet can be given higher valuethan the second bet, given equal terms, in order to increase thebenefits of early bets that become trend setting. This will make itvaluable to hang on to early bets, especially if their value depends notonly on a product but also its associated product category. Aspect 2910describes whether the bet relates to products to each other or not, andis a relation parameter. The term “Product A is better than product B”is a relative term, whereas “Product A is the best social network forteens” is directly not relative to another named product, and istherefore absolute. Aspect 2920 describes whether the term has acondition. For example, the term “Product A is better than product B”has no condition, whereas the following, and more complex, term does:“Product A is better than product B. If product B ceases to exist, thenproduct A will remain (because it is a better product).”

As was described in FIG. 26 , each term, such as first descriptor 2606has an opposite term such as descriptor 2607. For example, if firstdescriptor 2606 is “Product A is better than product B” then seconddescriptor 2607 is “Product A is not better than product B”. A coin usedto create one or more bets, such as the various coins described abovewith respect to FIG. 26 and below with respect to FIG. 30 , can have oneor more pairs of descriptors, where each such pair is described byaspect 2900, 2910 and 2920. As can readily be appreciated, the specificimplementation of a smart contract that is utilized to generatebetting-related recommendations, and/or the number of propositionsand/or the number of different sets of propositions is largely dependentupon the requirements of a particular application.

In one embodiment, bets marked with an indefinite termination point havethe ability to halt or move to a next phase of a smart contract upon atrigger event, such as (but not limited to) when a coin becomesspendable. The trigger event is likely to be provided by an oracle, suchas (but not limited to) one that would determine whether the marketshare example above has fallen below 25%. Oracles, long consideredmostly-trustworthy, but prone to occasional error, can be pre-selectedat the creation of the smart contract and policed with a bounty hunterincentivized to detect a fraudulent or mistaken oracle analogously tohow bounty hunters are awarded for their efforts, as disclosed in theco-pending application titled “Perpetual NFT Assets”, which isincorporated in its entirety by reference. The bet's associated smartcontract can be constructed with a policy that enables the coin tobecome spendable in spite of the indefinite term. Using these or relatedtechniques, the contract could be coded for this example to represent:User betting that the market share of Product A will drop to below 25%as determined by oracle A shall be able to spend 100% of the associatedcoin if oracle A has documented in an immutable ledger the measuredoutcome. In the market share example, the oracle Acme Corporation is anindependent market-leading market-research company well known forproviding quarterly market share reports that the parties agree are thegold standard for this contract. Multiple oracles, which are exampleservice providers, can compete to evaluate a situation or service andprovide indications of events where these indications can be used bysmart contracts to trigger actions. The same smart contract may providean incentive to both the oracle and a bounty hunter that polices theaccuracy of the oracle's integrity. For example, the smart contract mayinclude a fraction of the coin to be issued to the oracle for quarterly,annual, or trigger event, such as a drop below 25% market share byProduct A, for services rendered. In one embodiment, the smart contractmay include a policy to ensure sufficient delay in spending availabilitysuch that bounty hunters have an opportunity to review the integrity ofthe outcome and documentation. The smart contract policy may be to shiftownership of the fractional coin to be issued to the oracle would shiftin part, or in total, to the bounty hunters. In the event that a bountyhunter is able to prove a false outcome. As can readily be appreciated,oracles and bounty hunters can serve a variety of roles in ablockchain-based NFT platform that includes a recommendation platformimplemented using smart contracts in accordance with various embodimentsof the invention. Accordingly, the specific triggers incorporated withinsmart contracts utilized by the recommendation platform and/or theinformation derived from oracles and/or bounty hunters used in thetriggering of the smart contracts is only limited by the requirements ofspecific applications.

The bet-based recommendation mechanism is related to the notion used infutures contracts, but is not the same. For example, futures contractsalways have a delivery date, i.e., is finite (see end parameter 2902 ofFIG. 29 ). However, recommendations typically do not have a deliverynotion associated with them. A bet-based contract such as what isdisclosed herein, if finite, can assume a value that is determined bythe market price of the associated type of contract at the time of theexpiration date, at which time the associated structure becomes a coinof the currency with a number of units associated with the market price.For example, if the term of the bet is “Product A will be the marketleader on Jun. 15, 2028”, then Jun. 15, 2028 is the expiration date. Ifan associated set of terms, which may be specified in the contract ofthe term of the bet, is the collection of all bets that are positive ofproduct A (or otherwise indicated by one or more binary indicators inthe associated contract), and the average market price of these at theexpiration date is $38.25, then the coin associated with the bet“Product A will be the market leader on Jun. 15, 2028” will be assigneda value that correspond to the number of units of an associated currencythat has the value $38.25. Thus, at this point, in spite of theexpiration having passed, the coin still has a value. This value,though, will not fluctuate based on product A, but only based on beingspent by its owner. The non-fluctuating portion of the value can bespent as other coins without associated bet-enablement. Thebet-component can be sold. After the bet-component has converted, whenapplicable (such as at the expiration of the terms) then this componentbecomes a non-fluctuating portion, and can be spent as a regular coin isspent.

Similar to above, if a condition parameter specifies that there is acondition, then there may be a similar assignment of value based on suchcondition. For example, if a bet of a coin specifies that “Product A isbetter than product B. If product B ceases to exist then the value ofthis bet becomes the value of a basket of indicated bet types.” then asimilar conversion may take place. When a bet is converted in this way,that does not imply that it is not considered anymore when determining arecommendation from the bet. This determination is policy based, and mayin some instances use both expired and non-expired bets.

A coin that can be utilized to mint smart contracts used to buy and sellrecommendations in accordance with an embodiment of the invention isconceptually illustrated in FIG. 30 . The coin 3001 can be used to mintdocuments used to buy and sell recommendation opinions, which is avariant of the coin 2601 of FIG. 26 . However, instead of one public keyfor each descriptor (such as a first public key 2604 associated withfirst descriptor 2606 and second public key 2605 associated with seconddescriptor 2607), coin 3001 uses public key 3004 for verification of anysmart contract associated with descriptors, including first descriptor3006 and second descriptor 3007. In many embodiments, the proof data3003 can be a function of ledger reference 3002 and the public key 3004.In several embodiments, the first descriptor 3006 and second descriptor3007 can be tied to the public key 3004. In a number of embodiments,this can be achieved by having the private key 3008 associated with thepublic key 3004 sign the first descriptor 3006 and the second descriptor3007. In certain embodiments, the descriptors can be tied to the publickey 3004 by having the proof data 3003 be a function of the firstdescriptor 3006 and the second descriptor 3007.

As with coin 2601 discussed above with reference to FIG. 26 , coin 3001may have additional descriptors (not shown). In addition, the public key3004 may also be used to represent values not related to the firstdescriptor 3006 and the second descriptor 3007, and used to verifytransfers of value in a similar manner to public key 2504 (withcorresponding private key 2505 used to generate digital signatures thatencode said transfers of value). Thus, coin 3001 may have two functions:carrying a monetary value associated with the successful mining of thecoin 3001, as is traditional, and carrying a value associated with theorigination of bets, encoded by first descriptor 3006 and seconddescriptor 3007. Coin 3001 can also includes additional descriptors,according to a format specified for the coin 3001, one aspect of whichis described above with reference to FIG. 27 .

Two smart contracts that can be utilized for automated trading areconceptually illustrated in FIG. 31 . A first smart contract 3100includes limit parameters 3101 including buying limit 3102 and sellinglimit 3103, public key 3111 associated with private key 3112.

While specific constraints are described above with respect to smartcontracts that implement bet-based recommendations within arecommendation platform, smart contracts that are relied upon in theimplementation of recommendation platforms in accordance with variousembodiments of the invention are by no means limited to use with respectto bet-based recommendations and can be utilized in a variety ofapplications including bet-based recommendations, where the smartcontracts are configured to gather information and/or trigger inresponse to events in a way that enables the recommendation platform tospecific recommendations that and/or more precise recommendations. Whilemuch of the discussion above references recommendations made to userswith respect to the purchase, interaction, and/or acquisition of aresource, recommendations can also relate to the acceptance of offers tosell, dispose, allow interaction with a resource, and/or provideservices. Processes for generating offeror recommendations in accordancewith various embodiments of the invention are discussed further below.

Offeror Recommendations

In several embodiments, the recommendation processes described hereinhave usefulness to an offeror (e.g., an artist such as (but not limitedto) a tattoo artist). Identifying trends and appropriate pricepredictions leads buyers to good deals; however, the same technique canbe applied by the offeror to identify prices, discounts,advertising/promotion, and/or special deals to maximize total profit orpopularity, regardless of whether the item is trending up, down, orstable. For example, a tattoo artist in Atlanta has developed artworkcoinciding with a 20th anniversary celebration that he would like tooffer as a digital image represented by a non-fungible token. The artistoffers 100 limited time images and the NFT format data specifies thesize of the jpg encoded image, the color palette, whether the image maybe reproduced by a tattoo artist, and what applications are allowed torender it, if such a limitation exists. In circumstances in which salesof the 100 limited time images begin to stall after 25 sales. Arecommendation platform in accordance with an embodiment of theinvention, having identified a slowing trend may trigger a small pricedecrease, or alternatively, a payment to a popular influencer to revivesales. An account associated with the influencer may optionally beincluded in a smart contract enabling the release of influencerdocumentation once the algorithms have identified the appropriate salestrend. In another example, Company A competes with Company B in theautomobile air freshener market. Company B has an unfortunate chemistryproblem causing their air fresheners to go stale within 3 days of use.Consumers, finding fault with Company B rapidly shift to Company A'sproduct hoping for better quality. A recommendation platform inaccordance with another embodiment of the invention, noticing unexpectedopposite shifts in desirability score between Company A and Company B,can alert Company A to both increase production and product pricing tomaximize profit. As can readily be appreciated, the processes describedabove enable the generation of recommendations with respect to any of avariety of aspects of any number of different resources. Accordingly, aperson of ordinary skill will readily appreciate that the specificrecommendations provided to offerors by recommendation platformsimplemented in accordance with various embodiments of the invention areonly limited by the requirements of specific applications.

A process for generating recommendations for an offeror in accordancewith an embodiment of the invention is conceptually illustrated in FIG.28 . As in FIG. 23 , the system obtains origin data 2301, format data2302 and appreciation data 2303, and generates a scarcity score and adesirability score 2304. The scarcity score and desirability score 2304can be generated from the inputs 2301, 2302 and 2303, and may use arule-based algorithm, an ML based algorithm, or a combination thereof.The desirability score between two competing products is likely to shiftwith time as identified by the trend data 2306. For example, twoneighboring fuel stations may sell the exact same commodity product, butword is starting to spread about fuel station A having a credit cardskimmer problem at the pump. As the popularity of one product, fuelstation B, increases, while fuel station A decreases. The price estimate2307 can change triggering a recommendation to one or more of theofferors to adjust price, or in this case, to issue an influencerrecommendation 2808 to combat the skimmer news.

In the illustrated embodiment, the computation of the price estimateuses at least one of the scarcity score and the desirability score 2304,timeframe data 2305, trend data 2306, and may also use as input anasking price (not shown). As can readily be appreciated, the specificdata that is utilized to generate a price estimate is largely dependentupon the requirements of the specific application. The timeframe datamay indicate for what time period a price estimate is to be computed,e.g., “right now,” “in three months”, “for the next 24 months”, and soon. The trend data 2306 may indicate price trends, appreciation datatrends, scarcity trends, etc., for other items in general; for otheritems of the same genre; for other items by the same origin, and forother items with related format data. The price estimate may be computedusing a combination of statistical methods, ML methods, and methods suchas those described above.

While specific processes are described above for generatingrecommendations for offerors with reference to FIG. 28 , any of avariety of processes for generating recommendations for offerorsseparately from and/or in combination with generating offers for usersinterested in receiving/accepting offers can be utilized withinrecommendation platforms implemented in accordance with variousembodiments of the invention as appropriate to the requirements ofspecific applications. Furthermore, such recommendation platforms can beutilized in any of a variety of blockchain-based NFT platforms including(but not limited to) the blockchain-based NFT platforms and rich mediaplatforms described above with reference to FIGS. 1-17 . A number ofother useful applications for smart contracts within recommendationplatforms are discussed further below.

Grouping Items

Recommendation platforms in accordance with many embodiments of theinvention determine the similarity or dissimilarity of resources and/oritems as part of the process of generating recommendations. In a numberof embodiments, information concerning the similarity of resourcesand/or items can be utilized to ascertain likelihoods that user actionswith respect to a particular resource and/or item is informative withrespect to another resource and/or item. In several embodiments,recommendation platforms utilize resource and/or item characteristics todetermine similarity and/or create collections. In a similar way,resource and/or item characteristics and/or the shared characteristicsof collections of items can be utilized in the training of machinelearning models that can be utilized in the determination of resourceand/or item similarity and/or in the generation of recommendations asappropriate to the requirements of specific applications.

A process for grouping items with the intent of forming a recommendationin accordance with an embodiment of the invention is conceptuallyillustrated in FIG. 32 . Items 3201, 3202, and 3203 are similar virtualor real items such as (but not limited to) songs, motorcycles, andartwork. In this example, items 3201, 3202, and 3203 represent tattooartist artwork with customized renderings of the flag of the UnitedStates of America. Each item, from one or more artists includesimportant characteristics such as coloring, size, restrictions onreproductions, etc. These characteristics, which may be indexed with theitem, can be placed in a container of item characteristics 3211, 3212,and 3213 corresponding to items 3201, 3202, and 3203. In a number ofembodiments, these item characteristics are formatted as feature vectorsin the manner described above. In certain embodiments, the featurevectors are formatted to be utilized as inputs to a machinelearning/artificial intelligence model. A space 1520 can be created tocontain the collection of item characteristics to be used by the machinelearning/artificial intelligence algorithms including (but not limitedto) those described above. The illustration depicts three items forsimplicity of explanation, but it should be readily understood that alarge number of items can be utilized in the training of machinelearning models.

In many embodiments, the representation illustrated in FIG. 32 can alsobe utilized within a process for abuse detection whereby Item 1 3201,Item 2 3202, and Item 3 3203 are users that may be abusing the systemand container of item characteristics 3211, container of itemcharacteristics 3212, and container of item characteristics 3213 mayrepresent the various behaviors and characteristics of potential abuserssuch as (but not limited to) the number of transactions per hour orminute, change in value of total assets of the user within the system,changes in value of the items the user is browsing, positive reviews,negative reviews, bet frequency and size, and user profile information,along with IP address switching, number of bank accounts, number ofcredit cards, number of windows open accessing a portal at the sametime, number of devices from a single IP address, number of timeschanging password, number of times failed to login correctly, number oftimes change user profile information.

Referring now to FIG. 33 , a space with a collection of 16 items andtheir corresponding characteristics in accordance with an embodiment ofthe invention is illustrated. The expansion of space 3220 of FIGS. 32 to16 items in space 3300 of FIG. 33 leads to a remapping of the itemcontainers in FIG. 34 . Space 3300 contains 16 item containers which,for example, may simply be an expansion of the US flag tattoo collectiondescribed above. The collection includes item containers withcharacteristics 3301, 3302, 3303, 3304, 3311, 3312, 3313, 3314, 3321,3322, 3323, 3324, 3331, 3332, 3333, and 3334. The ordering of theseitems may be random or intentional. In several embodiments, thecollection of item containers can be simplified using dimensionalityreduction processes including (but not limited to) principal componentanalysis (PCA), whereby each item, in this case an NFT representing theflag tattoo, still maintains knowledge of the full N-dimensional featurevector.

With specific regard to FIG. 34 , a space 3400 with a collection of thesame item containers from FIG. 33 (i.e., 3301, 3302, 3303, 3304, 3311,3312, 3313, 3314, 3321, 3322, 3323, 3324, 3331, 3332, 3333, and 3334) isillustrated. The collection is transformed using machinelearning/artificial intelligence to self-organize the NFTs using eachitem's N-dimensional feature vector to result in, what may be, but notlimited to, a lower dimensional (e.g., a 2 or 3 dimensional)self-organized map, of the collection of items. A skilled artisan willrecognize that variations on these methods can also be used forself-organization including with an artificial neural network usingbackpropagation or Bayesian learner.

In the illustrated embodiment, sub-space 3401 identifies the items thatcorrelate to the desirable or undesirable characteristic, whichever thecase may be. In this example, item container 3312 is a reference itemconsidered desirable based upon the characteristics being evaluated anditem containers 3313, 3322, and 3324 are similar based upon use ofalgorithms to recommend a similar item including (but not limited to)Euclidean distance and K-nearest neighbor. Item container 3331 might beconsidered to be least similar. As can readily be appreciated, any of avariety of techniques for identifying similar item containers can beutilized as appropriate to the requirements of specific applications.

The same methods and tools described above may identify trends basedupon changes to the feature vectors with time. In several embodiments,the system may organize virtual art work by genre and use a measure suchas (but not limited to) Euclidean distance or K-nearest neighbor torecommend a similar item to a prospective buyer. In certain embodiments,a recommendation platform can continually learn by periodically (e.g.,daily) running the algorithms within the space to monitor for changes incharacteristics toward an effort to produce refined outputrecommendations. The recommendations made by users, reviewers,influencers and/or other parties may be weighted or filtered forauthenticity or genuineness as described with respect to FIG. 35 below.

Another example of grouping items based upon item containers and/orfeature vectors is the characterization of music for the purposes ofmaking a recommendation. In a music example, the sub-space 3401 mightreflect the hip-hop genre, while other sub-spaces, not shown, withinspace 3400 may reflect blues, heavy metal, and jazz.

While various approaches for grouping items into collections of similaritems/separating items into groups of dissimilar items and utilizinggroups to train machine learning models are described above withreference to FIGS. 32-34 , any of a variety of processes can be utilizedto group collections and items, describe items based upon theircharacteristics and/or train machine learning models can be utilized asappropriate to the requirements of specific applications in accordancewith various embodiments of the invention. The use of machine learningmodels (and other processes) to determine opinion relevancy and/ordetect abuse within recommendation platforms in accordance with variousembodiments of the invention is discussed further below.

Determining Opinion Relevancy and Detecting Abuse

Recommendation platforms in accordance with a number of embodiments ofthe invention can employ processes for determining opinion relevancyand/or detecting abuse (e.g., paid reviewers providing inauthenticrecommendations and/or reviews). In several embodiments, abuse detectioncan be performed using self-organized map infrastructure and/or using ahuman-tuned model based upon feature vectors. The feature vectors fordetecting abuse can include (but are not limited to) items such as thenumber of transactions per hour or minute, change in value of totalassets of the user within the system, changes in value of the items theuser is browsing, positive reviews, negative reviews, bet frequency andsize, and user profile information. An artificial neural network cancreate the N-dimensional space and processes such as (but not limitedto) principal component analysis can be used to create a lowerdimensional space (e.g., a 3 dimensional space). In this lowerdimensional space, the recommendation platform can search for anomaliesand/or trends. Once a first abuse is found, it is likely that otherabuses reside within the K-nearest neighbor range. It should be readilyappreciated that feature vectors can be analyzed in this way using avariety of machine learning and/or statistical modelling techniques andthat the processes utilized by recommendation platforms in accordancewith various embodiments of the invention are not limited to the use ofartificial neural networks or principal component analysis.

In several embodiments, processes for abuse detection rely upon auser-specific self-organized map that is based solely on usersthemselves as nodes. The feature vectors can include (but are notlimited to) items of behavior of the user (e.g., IP address switching),number of bank accounts, number of credit cards, number of windows openaccessing a portal at the same time, number of devices from a single IPaddress, number of times changing password, number of times failed tologin correctly, number of times change user profile information. In anumber of embodiments, this specialized self-organized map usesartificial neural networks to automatically cluster the feature vectors,and allows the recommendation platform to find abuse, and in the samearea where abuse is found, there is likely surrounding users that areabusing as well. This allows both individuals and a group or subset ofusers to be flagged for review and, or action. As can readily beappreciated, the specific manner in which feature vectors and/orself-organized maps are utilized to detect abuse is largely dependentupon the patents of abuse that are prevalent within a particularblockchain-based NFT system. Furthermore, processes that can be utilizedto identify users that are taking actions that are likely to generatemisleading recommendations can also be utilized to generate users thatare taking actions that are likely to form the basis of highly relevantrecommendations.

A process for computing suggestions, rankings, recommendations, andsimilar in accordance with an embodiment of the invention is illustratedin FIG. 35 . The process 3500 includes determining (3501) theauthenticity of a reviewer. For example, if the account of the revieweris newly created and has an unusually large volume of reviews, e.g.,exceeding a threshold T that may be set to 100 for a time of t, that maybe set to 96 hours, then the reviewer is determined to likely beinauthentic. In certain embodiments, each reviewer or other source ofdata can be given a score S, where S=100 may indicate that the party isvery credible, S=0 indicates that the party is not at all credible, andS=75 indicates that the party is more credible than most. A variety ofapproaches to scoring these parties can be used.

The authenticity of an individual review associated with the reviewercan be determined (3502). For example, a review including both of textand one or more scores can be assessed based on the text. If the textincludes many words that are indicative of a lack of hands-on knowledge,such as vague expressions of enjoyment or lack thereof, then the textmay be given a low authenticity score. If the same text has been usedfor other reviews, especially by the same reviewer, or the text issubstantially similar to those of other reviews, that also affects theauthenticity score of the review negatively. As can readily beappreciated, specific processes that can be utilized for evaluating therelevancy of content associates with a user's review is largelydependent upon the requirements of specific applications.

In several embodiments, a relevance matrix can be computed (3503), or ifit has already been computed and stored, retrieved from memory. Therelevance matrix can indicate topics that the reviewer is knowledgeableabout, as determined by factors including (but not limited to) havingmany reviews related to, by having reviews with high authenticityscores, and by having reviews that consuming users or automated agentsrate as helpful or not helpful. This can also be implemented usingstandard reviews tied to user names and/or other identifiers, or bybid-based structures, as disclosed herein. Thus, feedback in a review isa review by itself, and the same techniques disclosed herein can be usedto adjust the relevancy score of a review based such feedback. Inaddition, feedback of this type can be implemented by having bountyhunters identify reviews and other related expressions of opinions thatare likely to be fake or not helpful, where the veracity and value ofthe feedback provided by a bounty hunter is determined by the trendsthat follow the report. Thus, if a bounty hunter files a report statingthat a given positive recommendation is not helpful or is notrepresentative of the likely future trends, and later the value of theassociated opinion falls, e.g., by at least 10%, then the bounty hunterwas correct and is provided a reward that may depend on how many otherbounty hunters provided the same feedback, or the bounty hunter maychoose to bet against the product or service in order to receive afinancial reward for having detected a disingenuous recommendation. Anautomated agent can assess that a review is helpful when the reviewmakes a prediction, e.g., that a service will be increasinglyappreciated onwards, and the automated agent determines that thisprediction was correct.

Thus, relevance may be monitored with time, particularly if a “was thisreview helpful” outcome necessitates a re-ranking. The relevance matrixmay indicate that a given reviewer is knowledgeable of audio equipment(with a relevance score of 0.92 out of 1 for reviews related to audioequipment), rather knowledgeable of jazz music (with a relevance scoreof 0.6 for reviews of jazz musicians and jazz songs), but not veryknowledgeable of restaurants (with a relevance score of 0.21 for reviewsof restaurants, e.g., due to a common failure to correctly assess trendsin restaurant preferences). In step 1804, a relevance matrix is computedfor a target group of users, such as a user requesting a recommendationor a collection of users corresponding to the users attending a socialgathering. This, like the relevance matrix, may also be retrieved, ifalready computed, and indicates what areas of knowledge the target grouphas. As can readily be appreciated, any of a variety of techniques canbe utilized for generating a relevancy matrix based upon informationincluding (but not limited to) user profiles, content associated withactions (e.g., reviews), and/or information derived from externalsources (e.g., bounty hunters) as appropriate to the requirements ofspecific applications in accordance with various inventions.

A set of weights can be computed (3505) from relevance matrices. In manyembodiments, the set of weights can indicate similarity between a sourceof a recommendation and a target group, where a high similarity resultsin a large weight and a low similarity results in a small weight. Forexample, the weight for one topic may be the relevance element of thattopic associated with the recommendation source, times the relevanceelement of that topic of the target group. As can readily beappreciated, any of a variety of processes can be utilized forgenerating weights as appropriate to the requirements of specificapplications in accordance with various embodiments of the invention.

In many embodiments, an impact can be computed (3506). In severalembodiments, the impact is a function of a recommendation or other dataassociated with the source, and of the weight. If the reviewer rates arestaurant as having score s=9 out of 10, and the weight is a valuew=0.8, then the impact may be (s−m)*w+m where m is the midrange of thescore range, i.e., m=5 in this case. Thus, the computed impact in thisexample is (9−5)*0.8+5=8.2, corresponding to a high score. In the caseof a negative review corresponding to score s=2 and a low weight w=0.1,the resulting impact is (2−5)*0.1+5=4.7. This second impact score ismuch closer to the average value m, and therefore is less important thanthe review with an impact score of 8.2, since this affects the deviationfrom average much more. An alternative of this approach is to let theweights affect the probability that the review will be included in aMonte Carlo simulation in which reviews are drawn at random and a scoreis computed from iterated simulations. As can readily be appreciated,any of a variety of processes can be utilized to determine impact basedupon recommendation data and/or other sources of data as appropriate tothe requirements of specific applications in accordance with variousembodiments of the invention.

FIG. 36 illustrates the opinions expressed by user entity 1, 3601, e.g.,using an endorsement or a purchase of a contract related to an opinion,and by user entity 2, 3602, both of which are part of the input layer3600. These opinions affect the values of node i 3621, node i+1 36922and node i+2 3623, where node i 3621 may correspond to a concrete notionsuch as “audio equipment” or an abstract notion that does not directlycorrespond to such a concept but which is meaningful in a machinelearning context. Based on weights between user entities, such as userentity 1 3601 and user entity 2 3602, and the nodes, such as node i3621, node i+1 3622 and node i+2 3623, the opinions of the user entitiesaffect the values of the nodes. The extent to which this is done isexpressed by weights such as weight w1 _(i) 3611 between user entity 13601 and node i 3621; weight w1 _((i+i)) 3612 between user entity 1 3601and node i+1 3622. Weight w1 _(i) 3611 may, for example, be generated(3505) in the manner outlined above or using a related process; or begenerated from the relevance matrix (3503) of the reviewers, asdescribed above.

Each user entity can be associated with each node using a weight,although in some instances, the weight may be set to zero, meaning thatthere is no influence. The nodes acquire values that are based on thevalues of the opinions of the user entities such as the opinionsexpressed by user entity 1, 3601, and of the weights, such as weight w1_(i) 3611. This value for the nodes may, for example, be computed as asum of the weighted opinions, or using another combination function. Theinternal nodes such as node i 3621 express the collective opinions ofthe user entities, which may be reviewers, for example. There may bemultiple layers of connected internal nodes, although only one suchlayer is shown here for denotational simplicity. The values of theinternal nodes can affect the values of the recommendations given to theoutput layer 3640, which may contain identifiers corresponding to thesame entities as in input layer 3600, or a portion of these. The outputlayer 3640 can correspond to recipients of recommendations, whereas theinput layer 3600 can correspond to providers of opinions. Output layer3640 can include user entity A 3641 and user entity B 3642. The elementsof the output layer can be connected to the interior nodes such as nodei 1921 using weights, such as z_(i)A 3631, z_((i+1))A 3632 andz_((i+2))A 3633, which have a similar functionality as weight w1 _(i)3611, weight w1 _((i+i)) 3612 and weight w1 _((i+12)) 3613, and whichare used to weight the inputs from the internal nodes, such as node i3621. This way, recommendations can be computed from opinions. A personof skill in the art will recognize that there are many variations ofthis approach that are possible.

A process for identification of potential abuse associated withbid-based recommendation mechanisms in accordance with an embodiment ofthe invention is illustrated in FIG. 37 . The process 3700 includesidentifying (3701) the market price for a positive opinion (e.g., acontract associated with a coin, where the contract contains a term thatexpresses a positive view of a resource, such as a product). The marketprice for a negative opinion related to the same resource can also beidentified (3702).

A transaction volume for the positive opinion can be determined (3703),where this volume is relative to a predefined time interval, such as thelast 10 minutes or the last 24 h. The transaction volume for thecorresponding negative opinion can also be determined (3704). Inaddition (not shown), measurements related to trends and variations inprice may be determined, as well as information, when known, related tothe identity or pseudonym of buyers and sellers of the associatedcontracts. Based on the collected information, a classification isperformed (3705). This classification may be, for example, “positiverecommendation trending up, high trust”, which may in additioncorrespond to a precise recommendation score value and a large weightvalue indicating high trust. The classification may also be anindication of risk, such as “risk for self-interested buying, lowtrust”, which may result in no new recommendation value being generated,and a low weight is output. The classification may also be that there isa bifurcation of opinions held, and indications of what groups like theresource versus do not like the resource are output, e.g., based onidentity or pseudonym information. For example, this classification mayindicate a recommendation score of 7 out of 10 for entities with strongcorrelation with node i 3621, as indicated by a large weight value forz_(i)A 3631, as well as a lower recommendation score of 2.1 for entitieswith a strong correlated with node i+1 3622, as indicated by a largeweight for z_((i+1))A 3632, for example. This determination (3706) ofthe recommendation score and associated weight can then be performed.

A process for identification of expected or suggested market pricing inaccordance with an embodiment of the invention is conceptuallyillustrated in FIG. 38 . In several embodiments, the recommendationplatform may estimate a new current market price for an item on its ownor in comparison to one or more similar competing products. FIG. 38illustrates a comparison of two products, product A and B. Product Acurrent price and trends 3801 represents the previous market pricing forproduct A along with its historical trends. Product B current price andtrends 3802 represent the previous market pricing for product B alongwith its historical trends. The prices and trends are considerations3810 along with, but not limited to, recommended weightings 3811 (suchas, but not limited to, those described above with respect to FIG. 37 ),Product A bets 3812, and Product B bets 3813 toward the generation of anew price estimate 3820 for one or more products, in this exampleProduct A or B, or both.

While a variety of processes are described above for determining therelevancy of recommendations and/or detecting abuse with reference toFIGS. 37 and 38 , any of a variety of processes can be utilized todetermine the extent to which a user action is irrelevant and/orindicative of abuse as appropriate to the requirements of specificapplications in accordance with various embodiments of the invention.Furthermore, recommendation platforms in accordance with manyembodiments of the inventions can utilize external sources ofinformation such as (but not limited to) bounty hunters in theevaluation of recommendations as is discussed further below.

Evaluating Recommendations Based Upon Information from Bounty Hunters

Information provided by bounty hunters may be utilized by recommendationplatforms implemented in accordance with various embodiments of theinvention to impact the weight of recommendations for products andservices, whether real or virtual. In certain embodiments, informationprovided by bounty hunters is utilized by recommendation platforms tomodify the rankings of reviewers based upon their recommendations, whichserve as predictions to be assessed at a later time.

A process for evaluating recommendations based upon information receivedfrom bounty hunters in accordance with an embodiment of the invention isconceptually illustrated in FIG. 39 . In the illustrated embodiment, arecommendation 3901 is made, whether by an individual, influencer, user,reviewer, organization, etc. The review may be categorized (3902) byreaders of the recommendation as helpful or not helpful. A bounty huntermay find the recommendation fraudulent (3903) or an inaccuraterecommendation identified (3904). A determination (3905) can be made toincrease or maintain a weight and/or a ranking based upon a helpful ornon-fraudulent recommendation; while a determination (3906) may be madeto decrease a weight and/or a ranking based upon the outcome of afraudulent or inaccurate recommendation.

In a number of embodiments, the process may issue (3907) a bounty reward(if applicable) to a bounty hunter. In certain embodiments, feedback,such as (but not limited to) an indication “Recommendation rated ashelpful” (3902) can be limited to users that have a good reputation; canbe limited to one feedback per user and target product and service; canbe limited to a maximum number of feedbacks per day; or can be set toignore feedback that is anomalous in quantity, distribution or type.This is done to avoid malicious users polluting the system with fakefeedback. Another way is to associate the feedback with a bet againstthe underlying product, or a bet against the reputation of reviewers inline with the recommendation. In many embodiments, a ML system is usedto create a weight that indicates the truthfulness of the feedbackprovider based on trends following the time at which the feedback wasgiven, where the truthfulness score is used when other decisions aremade, and may affect the purchase price of opinions. Units with lowtruthfulness score, e.g., below a certain threshold, may be penalized bynot being allowed to participate in some aspects of the distributedfunctionality of the system.

While various processes are described above for utilizing informationprovided by bounty hunters and/or incentivizing bounty hunters toidentify unreliable and/or reliable actions such as (but not limited to)reviews are described above with respect to FIG. 39 , any of a varietyof processes can be utilized by recommendation platforms based uponinformation provided by bounty hunters and/or to provides incentives tobounty hunters as appropriate to the requirements of specificapplications in accordance with various embodiments of the invention.

As discussed in detail above, recommendation platforms in accordancewith several embodiments of the invention utilize user profiles. As isdiscussed further below, the generation of user profile information inblockchain-based NFT systems can be challenging. Accordingly, a numberof processes for automatically aggregating user information and buildinguser profiles in accordance with various embodiments of the inventionare described herein.

Systems and Methods for Automatically Generating User Profiles

The ability to provide relevant recommendations and/or effectivelytarget advertisements and promotions is often reliant upon availabilityof information regarding the interests and/or preferences of particularusers. Blockchain entries contain valuable, but indirect, informationabout user preferences and token ownership. However, these are notcurrently useful for purposes of determining preferences and improvingcontent recommendations. Recommendation platforms in accordance with anumber of embodiments of the invention generate user profiles associatedwith identifiers, such as public keys associated with ownership records.In several embodiments, a user profile describes not just a currentstate, such as what the user associated with the public key currentlyowns, but also past ownerships, both in terms of crypto currencies andtokens, such as non-fungible tokens (NFTs). NFTs can be associated withgenres, such as visual arts, movies, music, non-music audio. They canalso be associated with likely acquisition reasons, such as investment,consumption, and creation for the purposes of sale or renting. They canfurther be associated with the origin of the NFT, e.g., whether it wasgenerated by a famous musician, by an underground musician, or by theuser who owns the NFT.

In many embodiments, recommendation platforms use information regardingtransactions between accounts recorded in an immutable ledger to inferinformation regarding relationships between individual users. Aninteraction between wallets can indicate potential social relationshipsor recurring acquisition patterns, e.g., whether a user associated witha first public key has acquired several NFTs from a person with a secondpublic key. The profile of the second user, associated with the secondpublic key, may therefore provide information about the profile of thefirst user, associated with the first public key. In some instances, thepatterns of transfers may indicate that the two users are the same, andthat the two public keys correspond to two aliases of the same user. Inother instances, the patterns of transfers may indicate a familiarrelationship, e.g., where several highly sought after NFTs are acquiredby one user from another user.

Analysis of the recorded price of tokens can identify whether a userassociated with the tokens is likely to select tokens for purposes ofinvestment or not, and whether he or she beats the market in terms ofthese investments. The timing of acquisitions can also indicate whethera user is an alpha adopter or not, e.g., if tokens from a creator thatlater trended were purchased by a given user. These are indications thatdetermine the user's likely preferences, e.g., whether the user wantsthings that have already been proven valuable by the actions of others,or if the user has a good understanding of what is likely to laterbecome valuable. It can be described as a trendiness indication of theuser.

Estimates of flows of financial assets, along with current ownership,indicates the likely disposable income of the user, and how this ischanging over time. This helps identify what type of expenses may betolerable to the user. Analysis of content associated with owned andpreviously owned assets, such as NFTs, indicate preferences, e.g.,whether a user prefers visual arts to music or vice versa. It also mayindicate genres of preference, e.g., whether the user prefers rock tojazz. As can readily be appreciated any of a variety of pieces ofrelevant information can be inferred by reviewing transactioninformation from immutable ledgers associated with public keys inaccordance with various embodiments of the invention. Recommendationplatforms and processes that utilize transaction information recorded inimmutable ledgers to generate user profiles in accordance with variousembodiments of the invention are discussed further below.

Automated Profile Generation

Recommendation platforms in accordance with many embodiments of theinvention build profiles and generate associated characterizations basedupon information including (but not limited to) transaction datarecorded in immutable ledgers. An example characterization may have (butis not limited to) a demographic component, a financial component, agenre component, and a trend-setter component, each one of which may berepresented by one or more scores. Demographic components, e.g.,indicating likely age and gender, can be built by correlating ownershipto known preferences of various demographic groups, e.g., a liking for agiven K-pop artist may indicate that the associated user is likely to bea teenage girl. A liking for Formula-1 racers known to be popular amongwealthy middle-aged men indicates a likely demographic group thatcorresponds to wealthy middle-aged men. Combinations of demographicassessments may strengthen such predictions by corroborating one usingthe other. Combinations of demographic assessments may also indicatethat a given profile corresponds to multiple users with a highlikelihood. As noted above, the specific characterizations that can bedetermined based upon transaction data recorded within an immutableledger are largely only limited to the specific transaction datarecorded within the immutable ledger and the requirements of a givenapplication.

A process for mining data from one or more blockchains to build and/orupdate user profiles in accordance with an embodiment of the inventionis illustrated in FIG. 40 . The process 4000 includes scanning (4001)blockchain entries and recording data associated with public keys. Datainclude tokens, such as non-fungible tokens (NFTs) and crypto funds, aswell as transactional indications such as who obtains such elements fromwhom. In addition, but not shown in the figure, non-blockchain data maybe scanned, e.g., from social networks. As can be appreciated, any of avariety of data recorded in immutable ledgers and/or sourced fromadditional external sources of data may be utilized as appropriate tothe requirements of specific applications.

One or more profiles can be generated (4002) based on clustering theobtained data. One way of clustering data uses public keys associatedwith the data. Such public keys can also be correlated with otheridentifiers, such as social media handles, and data associated with theidentifier included in the profile as well. As can readily beappreciated, any of a variety of techniques and/or sources of data canbe utilized to perform clustering as appropriate to the requirements ofspecific applications in accordance with various embodiments of theinvention.

In addition, one or more characterizations (4003) can be generated forthe profiles. A characterization may indicate (but is not limited to)one or more interests or ownership descriptions of a user associatedwith a profile, a demographic description of the user, a prediction ofan interest, or a combination of such attributes. The one or morecharacterizations may be stored as part of the profile, or associatedwith it.

In many embodiments, a request is received (4004) related to anidentifier associated with a user profile and a response to the requestcan be determined (4005) based on the identifier and with one or morecharacterizations related to a user profile associated with thisidentifier.

In addition to utilizing transaction data recorded in an immutableledger to build and/or maintain user profiles. Applications thatinteract with the relevant blockchain(s) can also directly provideinformation to recommendation platforms in accordance with severalembodiments of the invention. In many embodiments, a wallet applicationon a user device generates notifications corresponding to one or moretemplates, as disclosed in co-pending application titled “BiometricAuthentication using Privacy-Protecting Tokens” by Markus Jakobsson, andtransmits the notifications to the entity creating and maintaining userprofiles. These notifications are associated with the profile with thesame identifier, such as public key. The notifications may includedemographic information, or select information about user interests.

While specific processes for automatically generating and/or maintaininguser profiles are described above with reference to FIG. 40 , any of avariety of processes can be utilized that gather information fromtransactions recorded in immutable ledgers and/or received fromapplications that interact with immutable ledgers and build and/ormaintain user profiles in accordance with various embodiments of theinvention. Furthermore, the processes described herein can be utilizedin any of the recommendation platforms described above with reference toFIGS. 18-39 as appropriate to the requirements of specific applications.Processes that can be utilized to infer additional information from userprofiles in accordance with various embodiments of the invention arediscussed further below.

Automatically Inferring User Profile Information

The creation of characterizations from profiles can be done usingrule-based logic and statistical techniques based on correlation betweenobserved behaviors and known behaviors of various groups of users. Itcan also be performed using artificial intelligence (AI) methods, suchas machine learning (ML) methods.

Correlation between ownership records enables the determination that twodifferent users, represented by their associated public keys, havesimilar behavior, which may indicate that they know each other or thatthey have similar preferences. Timing based behavior may indicate thattwo users are likely to be in the same time zone. IP logs associatedwith records associated with the public keys may be used to determinemore specific locations, such as cities and even neighborhoods, withwhich users are associated. Data not placed on the blockchain can beused to determine social network connections, e.g., whether one usertweets that he bought an NFT and another user retweets it; thisdemonstrates a social connection between the two users. Also, suchexternal profiles, such as twitter data associated with a given user,can be used to augment user profiles with which the data can becorrelated or associated. For example, a user who transmits a tweetstating that he is selling a given NFT, linking to the NFT in amarketplace, makes it likely that the twitter profile is associated withthis user. Such information can be made part of the associated profileand the one or more characterizations that are produced from thisprofile. Accordingly, information that connects users can be utilized ina variety of ways to infer characteristics of individual users inaccordance with many embodiments of the invention.

In many embodiments, the characterizations are utilized to makepredictions related to likely preferences. A user whose wallets containa large number of in-game artifact is likely a gamer; a user whosewallet contains NFTs associated with famous artists is likely to bepassionate about art; a user whose wallet contains NFTs of pixelatedwhales are likely buying these due to the novelty or the potentialinvestment opportunity. The sophistication of a user in terms ofdistinguishing high quality items from lower-quality items in one genrecan be determined by the portion of items of the corresponding types inthe user's wallet. The wallet is associated with a public key that islinked to records on the blockchain, enabling the system to determinecurrent and past ownerships associated with a given wallet, as well asdetermining the purchase and sales prices. By having heuristics toassess the quality or type of items, the user interests can be assessedbased on past and current ownership records.

The characterizations can also be used to select actions to take. Oneexample action is the generation of a recommendation. Another exampleaction is the selection of an NFT to be gifted to a user. A thirdexample action is the selection of a promotional token to be provided tothe user in the context of a content request from the user. Userrequests may be generated, for example, by a user wallet, e.g., to helpits owner identify new content of relevance and avoid the user having tosearch for new content, e.g., using a search engine. A fourth exampleaction is the selection of content to be generated as disclosed in U.S.Provisional Patent Application No. 63/275,713 titled “User-SpecificEvolution, Spawning and Peeling” by Markus Jakobsson and Perry R. Cook.Another example action is the customization of a platform, such as amarketplace, based on the likely preferences of a user, wherein contentof types that are likely to be preferred is highlighted.

A process for determining user similarity in accordance with anembodiment of the invention is illustrated in FIG. 41 . The process 4100includes generation of multiple characterizations related to multipleusers, e.g., as described above with respect to FIG. 40 . The userprofiles can be clustered (4102) based on the characterizations and atleast one recommendation can be generated (4103) based on theclustering. Example recommendations include but are not limited to:introductions of users to each other; a suggestion for one user tofollow another user; based on a first action of a first user, suggest asecond action to a second user, where the first and second users areassociated with each other in the clustering and wherein the secondaction is related to the first action. One example action is a purchaseof an NFT of a particular content creator. As can readily be appreciatedthe specific recommendations that are generated are only limited by therequirements of particular applications.

At least one recommendation can be transmitted (4104) to a user, whereexample transmissions include (but are not limited to) promotionalcontent associated with requested content, rendering of suggestions inresponse to a search request, gifting of tokens, generation or selectionof content for evolution, spawning or peeling. Processes for identifyingand distributing promotional content is disclosed in U.S. ProvisionalPatent Application No. 63,210,040 titled “Content Recommendation Method”by Markus Jakobsson, Stephen C. Gerber, and Ajay Kapur, and in U.S.Provisional Patent Application No. 63/234,086 titled “Tokenization andPromotion of Authored Content” by Ajay Kapur, Stephen C. Gerber, MarkusJakobsson, and Madhu Vijayan. Evolution, spawning and peeling isdisclosed in U.S. Provisional Patent Application No. 63/275,713 titled“User-Specific Evolution, Spawning and Peeling” by Markus Jakobsson andPerry R. Cook.

While specific processes are described above with reference to FIG. 41for making recommendations based upon the similarity of user profilesassociated with users, any of a variety of processes can be utilized todetermine similarity and/or generate recommendations based upon userprofiles in accordance with various embodiments of the invention.Further processes for augmenting user profiles using additional sourcesof data in accordance with various embodiments of the invention arediscussed further below.

User Profiles Incorporating Partitions

In several embodiments, a user profile can also be based on a user'sassessments of data. For example, a user that rates an item, such ascontent that is related to or matches content in an NFT, may beassociated with the corresponding NFT, independently of ownership, wherethe association includes the rating, the like/dislike, or an assessmentof preference derived from a comment or a tag added by the user, e.g.,using natural language processing (NLP) techniques, or sentimentanalysis tools. The username associated with a social networking accountmay commonly be associated with a wallet, e.g., by identity claims madein a wallet or a social media profile. The ratings and other assessmentsmade by the user in a social media setting can then be used, in mannersanalogously to what is described above, to generate profile entries.

When a user borrows or rents content, e.g., in the form of an NFT, he orshe may leave reviews of the associated content, e.g., on review sites,or using derived tokens that refer to the NFT and which contain areview, a rating, or a statement. An influencer, for example, may dothis for purposes of promoting content, and non-influencers may do it toshare insights with their friends. Entries related to such assessmentswill be included in the profiles of the associated profiles. A userprofile can be partitioned into what the user says, e.g., retweets orotherwise promotes, and into what the user owns. Sometimes, this mayreveal different aspects of a user. It may also reveal multiple usersassociated with a given wallet. The partitions can be referred to asdifferent personae associated with a given wallet. In severalembodiments, user characterizations are made relative to one or moresuch partitions corresponding to a persona, and actions taken accordingto one or more personae associated with the wallet.

In many embodiments, temporal changes of interests are detected, e.g.,by generating characterizations spanning different time periods, whichmay be overlapping, and determining whether there are new interestsadded, old interests fading, or whether the characterization, e.g.,related to disposable income, changes over time. Such temporal sequencesof profile assessments can be performed and used to understand thedynamics of end users, and/or to determine a user's current interests.They can also be used to predict likely future interests, e.g., usingtrend prediction tools that correlate the evolution of interests amongtypical users and a given user in order to determine likely candidatefuture interests of a given user. Drastic changes of interests maysometimes indicate a risk that a wallet has been taken over, e.g., if auser suddenly and rapidly starts transferring ownership of large numbersor very valuable items in a wallet, without previously having performedsuch transactions. Whereas this may also indicate that the user simplywishes to do some profit-taking, that would commonly be associated withcorresponding purchases or investments. Sale of items at below-marketprices is another indication of risk, especially when observed for amultiplicity of wallet items. A user may subscribe to alerts fromservices that monitor his or her wallet for indications of risk.

In certain embodiments, a user subscribes to information about newcontent that the user may be interested in based on observed profiledata, and optionally, on user-provided goal characterizations, such asindications of intent and risk tolerance. One example intent is topurchase NFTs that are likely to substantially outperform a given indexwithin a 12-month period. The risk tolerance may indicate whether a userwould rather hedge by buying multiple NFTs from different creators, andof different types, or the user may be willing to make a smaller numberof less certain purchases for which the assessment is that there will beparticularly strong growth. These assessments may be determined based ontrend-models, e.g., trained on wallet data as described above, and whichidentify likely future trends for large numbers of users, therebyassessing the likely market moves.

Characterizations may indicate (but are not limited to) a demographicgroup, such as male 20-25 years old; an interest, such as an interest inanime dogs; a socio-economic identifier, such as “likely collegeeducated, annual income approximately $75,000”; a location, such asSeattle; and an objective, such as “interested in flipping investments”.Content may be selected based on characterizations, and/or based on bidsrelative to such characterizations. The selected content may be providedfree of charge, combined with other content such as user-requestedcontent, or provided as part of a service.

While specific processes are described above for automaticallygenerating profile data by analyzing transactions recorded in immutableledgers and/or gathering data from publicly available sources,recommendation platforms in accordance with many embodiments of theinvention can communicate with applications utilized by users tointeract with one or more immutable ledgers to gather user data.Recommendation platforms that communicate with end-user applications togather user data in accordance with various embodiments of the inventionare discussed further below.

Gathering User Profile Data Using End-User Modules

In several embodiments, an end-user module, such as an application,observes user behavior and generates a profile including one or moreclassifications of interest, and a log of events such as conversionevents and other notable events. Behavior, events and actions can becollectively referred to as events. One example conversion event is thepurchase of a product following the advertising of the product to theuser. An example of a notable event is an expression of preferences thatmay not be in response to an advertisement, such as a purchase that isnot related to a previously displayed advertisement. Another example ofa notable event is the establishment of a membership by a userassociated with the end-user module. For example, such a membership maybe an account with a given service provider, such as a publisher ofnews. Aspects of the notable event may also include configurationparameters, such as particular news preferences a user may set uponhaving subscribed to the online newspaper in this example. In severalembodiments, one or more classifications of the user profile mayidentify the user based on user-provided information, such asdemographic information that the user provides. This may include age,gender and zip code, for example. The classifications may also identifythe user based on his or her observed actions, where example actionsinclude what websites the user visits, how often, how long, and at whattime of the day. A classification may identify a person as likely to beinterested in purchasing a car, for example, based on such behaviors.

In certain embodiments, the end-user module is part of a user walletapplication, an application such as a social media application thatincludes functions like a wallet, a browser, or be part of the operatingsystem of the end-user device. End-user modules may coordinate acrossmultiple devices, e.g., coordinate the classification and loggingefforts associated with a given user as this user first performs actionson a laptop and later performs actions on a smartphone associated withthe laptop. This association can be determined, for example, by thecapability to access a resource, such as a financial account or a walletfor non-fungible tokens (NFTs) from both the laptop and the smartphone.The association can also be determined by explicit linking, e.g., by theuser, of the two devices. One such linking can be performed by scanning,using the smartphone, a QR code displayed using the laptop, e.g., whilethe user is logged in to the NFT wallet. In several embodiments, profiledata associated with one or more instances of an end-user module may bestored in one or more locations, including on an external device, suchas a server that is part of a cloud storage service used by the end-usermodule. In a number of embodiments, the profile may be periodicallyupdated, e.g., by synchronizing two or more contributing end-usermodules with respect to the data they have stored. The profile data isstored in a record associated with the end-user. Multiple users usingthe same device may each be associated with separate records, where therecord may be determined based on login data, biometric informationcollected by the end-user device, and/or by a user profile selection.Multiple users may also be associated with one and the same profile,e.g., when it is not practically possible to distinguish the users fromeach other or they are substantially similar to each other.

Processing of data by an end-user module implemented in accordance withan embodiment of the invention is conceptually illustrated in FIG. 42 .The end-user module 4200 processes user data 4201 using a dataprocessing unit 4202 resulting in the creation of a user profile 4203.The end-user module 4200 receives a request from a service provider4204, including a template 4205, where the template is conveyed to dataprocessing unit 4202. Data processing unit 4202 accesses user profile4203 and generates, based on template 4205 and user profile 4203 ananonymized profile 4206 that may be encoded as part of a usagestatistics token, and transmitted to service provider 4204.

An example of a template that can be utilized by an end-user modulesimilar to the end-user module described above with reference to FIG. 42is illustrated in FIG. 43 . The template 4300 includes an interestclassification 4301, such as “F1 racing”, or “basketball”; a demographicidentifier 4302, such as “male, 20-29 years old”; a temporal identifier4303, such as “within the last 14 days”; and an action identifier 4304such as “purchased NFT”. This corresponds to a template that would bematched by a person with exhibited interests matching interestclassification 4301, who belong or is estimated to belong to thedemographic group specified by demographic identifier 4302, and whowithin the period associated with temporal identifier 4303 has performedan action matching action identifier 4304. These are example fieldsbeing described. In one embodiment, a template 4303 would include afield descriptor 4305 that identifies what fields it comprises; examplesof these fields include interest classification 4301, demographicidentifier 4302, temporal identifier 4303, and action identifier 4304,each one of which may be identified by a set of bits specific to such afield.

In several embodiments, some of the fields of a template, such asinterest classification may be a selector that must be matched by theuser profile of a user for the data processing unit of an end-usermodule to generate a response (e.g., an anonymized profile) to thetemplate. In a number of embodiments, other fields may be a request forinformation such as demographic identifier. For each field, it would bespecified whether it is a selector or a request for information. Incertain embodiments, all fields are selectors; whereas in yet otherembodiments, all fields correspond to requests for information. Whilespecific templates are described herein with reference to FIG. 43 , itshould be readily appreciated that any of a variety of templates can beutilized as appropriate to the requirements of specific applications inaccordance with various embodiments of the invention.

In several embodiments, an anonymized profile may include responses tothe requests for information, as applicable, as well as any additionalprofile data generated by the data processing unit of an end-usermodule. Such additional profile data may be (but is not limited to)usage statistics, such as the number of NFTs in the wallet; theestimated value of the crypto currency associated with the wallet; thename of the most used application executing on the device on whichend-user module runs; etc.

While a variety of end-user modules and approaches to generatinganonymized user profiles in response to templates are described above, avariety of processes can be utilized to gather user data using end-usermodules and to build user profiles and/or anonymized user profile dataas appropriate to the requirements of particular applications inaccordance with different embodiments of the invention. Processes forgenerating tokens using user data in accordance with various embodimentsof the invention are discussed further below.

User Profile Derived Tokens

In several embodiments, the profile data stored in a record can be usedto derive one or more tokens. These tokens can include assessmentsgenerated from the data in the record. For example, one assessment maybe a demographic description of a user that identifies the user as amale in his thirties, with a likely disposable income of $26,000-32,000per year. Another assessment may be an interest, such as an interest inF1 racing, and a statistical assessment indicating that the interest inF1 racing is based on at least 25 observations, or has been determinedto be statistically significant with an estimated error rate of 3%. Incertain embodiments, the assessments may be created in response to aninput which includes a collection of templates, where each templatecorresponds to a type of assessment of interest to one or moreadvertisers or other service providers. For example, in one context, anadvertiser may only be interested in users with an interest in one ormore products that the advertiser sells, which may span interest rangingfrom horse riding and dog walking to parasailing and hunting. Thisadvertiser may not have any products related to watersports, andtherefore, may not be interested in knowing whether a given user is awindsurfer or not.

In certain embodiments, an end-user module, e.g., in the form of awallet or a browser plugin, may determine that the user associated withit has one or more interests associated with one or more of thetemplates expressed by a given advertiser, and generate a token thatincludes information expressing the user's interest aligned with thesetemplates. This token may be purchased by the advertiser, from the enduser module, or obtained in exchange for a service. The token may simplyinclude demographic information in cases where there is no match betweeninterests and templates. If this is so, the service provider may stilloffer the user a service, but potentially not the same service as theuser would receive if there is an expressed match to a template, and/orat a different cost than to a user with an expressed match.

In a number of embodiments, a token is generated by the end-user moduleto express one or more alignments with templates, and one or moredemographic assessments. These can be sufficiently general that theystill provide k-privacy, i.e., privacy for the user among k−1 otherusers with the same description. The generation of a token can be maderelative to other tokens to provide privacy assurances such ask-privacy, based on published statistics of the commonality of variousprofiles.

In a number of embodiments, a service provider may offer a service thatis configured based on the contents of a token. If the token expresses agiven alignment with a template and a given demographic, for example,then the service provider may select a first promotional offer oradvertisement to the user, as part of another service provision, whereasif the token expresses another alignment, or no alignment at all, thenthe service provider may provide a different service.

FIG. 44 illustrates the selection and distribution of promotionalcontent 4405, such as an advertisement, a discount coupon, or gift NFT.In the illustrated embodiment, a service provider 4401, which may be(but is not limited to) a provider of videos, music or otherentertainment, for example, has received anonymized profile, andtransmits a request 4402 to ad provider 4403. The request may optionallyinclude an anonymized profile, or data generated from an anonymizedprofile. The ad provider 4403 can respond with a selection ofpromotional data 4404, where this may be a reference to content. It mayalso be content, such as audio content. Service provider 4401 generatesand transmits a content response 4405 to end-user module 4406, where itis displayed on rendering unit 4407. The content response includescontent 4408 and promotional content 4409, which may be the same aspromotional data, or selected based on the promotional data. The contentresponse 4405 is received by the end-user module 4406 and sent, at leastin part, to rendering unit 4407, which may be a screen, a loudspeaker,one or more processors, etc. In several embodiments, a content response4405 may include audio, video, scripts and/or other data. Gift NFTs canbe selected in the manner described in U.S. Provisional PatentApplication No. 63/275,713 titled “User-Specific Evolution, Spawning andPeeling” by Markus Jakobsson, based on a commercial placement request,e.g., by ad provider 4403. In several embodiments, the service provider4401 and the ad provider 4403 are co-located, and may correspond todifferent executables running on the same system, for purposes ofreducing network lag associated with communication over a network. Ascan readily be appreciated the specific implementation of systems thatperform the functions described above with reference to FIG. 44 arelargely dependent upon the requirements of a given application.

Service providers in accordance with many embodiments of the inventioncan generate logs. A log 4500 generated by a service provider as aresult of the placement of promotional content for a user associatedwith end-user module in accordance with an embodiment of the inventionis conceptually illustrated in FIG. 45 . After transmitting a contentresponse to an end-user module, a service provider can add a firstrecord 4501 to log 4500. It may later add a second record 4502. In theillustrated embodiment, the first record 4501 includes an anonymizedprofile 4503, or a reference to it; promotional data 4504, or areference to it; and optional conversion data (4505) that may bereceived from an end-user module, or a similar transactional system, inresponse to the rendering of promotional content on a rendering unit,and optionally, based on a detected user action of relevance to thepromotional content. Examples of such user actions of relevance caninclude (but are not limited to) the purchase of an advertised product,the use of a gifted NFT, or the access to a website where the useraccessed an article related to promotional content. Information aboutconversions may be received from the end-user module during the samesession or at a later time.

In a number of embodiments, a log may be provided to an entityassociated with ad provider, for book-keeping purposes, but also forthis entity to determine the conversion rate, optionally based ondemographics associated with anonymized profile, the time of the day atwhich promotional content was rendered, or the context in which thepromotional content was placed. An example of such a context is theassociation with content that was requested by the end-user module,whether due to an explicit end-user request for this content or due toan assessment by the end-user module that the user would want content.Such an assessment can be made based on previous actions of the user (ora set of users), including conversions, as well as based on the userdata and the associated user profile. In certain embodiments, a log maybe recorded in a public database, such as a blockchain. Entries such asfirst record may be encrypted, e.g., using the public key of adprovider, or a symmetric key established between a service provider andan ad provider. In addition, the first record may have a non-encryptedindication of the identity of the party that can decrypt an encryptedportion of first record. Furthermore, access to the log data can bepermissioned using the techniques described above.

While specific processes for generating anonymized user profile data andgenerating logs are described above, any of a variety of processes canbe utilized in accordance with various embodiments of the invention.Furthermore, user data can be recorded within an immutable ledger usingtokens in a variety of additional ways discussed further below.

Another type of element that a token implemented in accordance with anembodiment of the invention may include relates to one or moreconversions. Service provider A may receive a conversion notificationthat indicates that the user may have performed an action that is aconversion with respect to an advertisement shown by service provider B.The conversion notification may be encrypted, at least in part, to hideits nature to any entity other than the service provider it relates to,e.g., service provider B in this example. As service provider A reportson the conversion, service provider A may be provided with a smallbenefit for the facilitation of the conversion determination, such as apayment of $0.005. The service provider that the conversion relates tomay transmit the token, or conversion data of the token, to a thirdentity, such as a manufacturer. This is entity C, and corresponds to theparty whose product was advertised by service provider B, leading to aconversion. As service provider B does this, it may be paid for theprovision of the advertising service, e.g., paid $2.50 or anotherpre-agreed amount. The amount may also depend on the nature of theconversion, e.g., how many units the user purchased. Here, serviceprovider B may be an influencer who may, for example, have provided aproduct review of the products of entity C.

Usage statistics may be specific to one or more specific artifacts. Forexample, a wallet or other user-side entity may generate an NFT thatspecifies actions taken relative to one or more NFTs, such as (but notlimited to) NFTs that are contained in a user wallet; NFTs that weresold and transferred away from the wallet within a ten-day period; NFTsthat were bought and added to the wallet within a ten-day period; NFTsthat a user associated with the wallet bid on but did not purchase; NFTsthat the user viewed within a ten-day period; NFTs that the usermodified within a ten-day period, where one example modification is apeeling event, as disclosed in U.S. Provisional Patent Application No.63/275,713 titled “User-Specific Evolution, Spawning and Peeling” byMarkus Jakobsson. Other time periods can also be used, as will beappreciated by a person of skill in the art. Instead of disclosing aunique identifier associated with an NFT associated with one of theabove criteria, the wallet may instead redact information by insteadspecifying the number of NFTs of the different types for which the userhas taken actions relative to, general classifications related to theseNFTs, such as whether they are image-based or audio-based; whether theyare classified as art or in-game artifacts; whether there are royaltypolicies associated with them or not, and so on. Royalty policies aredisclosed in U.S. Provisional Patent Application No. 63/281,721 titled“Royalty Sharing Method” by Markus Jakobsson.

Usage Statistics Tokens

In several embodiments, usage statistics tokens may include data relatedto (but not limited to) the execution environment(s) with which they areassociated; the user(s) they are associated with; the actions taken; thecontents of and changes of wallets associated with the tokens; and more.Some or all of these may be protected, e.g., encrypted or at least inpart redacted. The encryption may be performed relative to a public keyof a trusted third party or a symmetric key established with such aparty. In certain embodiments, data may be encrypted with a keyassociated with a buyer of data, such as an advertiser. This can be doneto ascertain that only this entity gains access to the data. Redactioncorresponds to the removal of at least some data, or replacement ofunique identifiers by classifiers, e.g., descriptors of types of data. Ausername is a unique identifier, for example, whereas the gender or zipcode of the user is a descriptor of the user that is not unique. Othertypes of redactions can be used, as will be appreciated by a person ofskill in the art. Yet another anonymizing approach is the use ofaggregation. By aggregating multiple data points, such as multiple useractions, to a profile describing the likely needs of a user or otheruser classification data, the privacy invasion associated withdisclosure of detailed information can be addressed. Thus, a wallet cangenerate a profile describing a user and encode this profile in a token,where the token can be provided in exchange for funds or services, whileprotecting the privacy of the user since detailed information is notdisclosed, nor is personally identifiable information (PII). In a Web2.0 setting, in contrast, advertisers will get as much data as theycould, where such data includes personally identifiable information aswell as information that a user may consider sensitive.

In a number of embodiments, usage statistics tokens indicate a degree ofchange from a previous time, where the previous time may be the time forthe most recent transfer of a usage statistics token or a triggeringevent, such as the display of a specified advertisement to the user of adevice associated with the generator of the usage statistics token. Thisenables the detection of the impact or influence of a given messagingcampaign, whether in general or relative to a particular set of selectedtopics, where an example topic is described by a template provided by aservice provider. In many embodiments, a client-side process such as anend-user module transmits, in response to a template, an anonymizedprofile that includes an indication of a match between the user profileassociated with the end-user module and the template. In certainembodiments, the anonymized profile includes data indicated in thetemplate. Thus, the template may include fields with at least twodifferent uses: the first use is a selection of whether the template ismatched, based on the profile data, and the other is a request for dataof a type indicated by the template. The anonymized profile may beencoded as a usage statistics token, but may also be transmitted withoutbeing tokenized. Example methods for tokenizing, as well as disclosuresof token types and functionality, are described in U.S. ProvisionalPatent Application No. 63/213,251 titled “Token Creation and ManagementStructure” by Markus Jakobsson and Stephen C. Gerber.

In many embodiments, the end-user module can determine conversions andconvey these to third parties. The anonymized profile, which may betokenized, may include one or more conversion data elements. Aconversion data element is a reference to a promotional content elementsuch as (but not limited to) a movie, a gift NFT, a visualadvertisement, an audio advertisement, or a coupon. The referenceidentifies one or more such promotional content elements. Furthermore,the conversion data element may include one or more indications of thenature of the conversion. Examples of such indications include but arenot limited to indications that a user visited a website, a userperformed a transaction such as a purchase, a user interacted with thepromotional content element, e.g., played a game associated with it,etc. The conversion data element may also indicate the context of theconversion, e.g., where a purchase was made, the price of a purchase,etc. In some instances, a conversion corresponds to a demonstration ofattention by a user to the promotional content, e.g., as determined bydetermining what the user viewed on a screen, what the user clicked on,whether the user was attentive, etc. The determination of attention canbe performed using methods disclosed in U.S. Provisional PatentApplication No. 63/219,864 titled “Using Tokens in Augmented and VirtualEnvironments” by Markus Jakobsson, Stephen C. Gerber, Ajay Kapur, andMadhu Vijayan. An anonymized profile may include data resulting fromperforming a survey, e.g., as disclosed in U.S. Provisional PatentApplication No. 63/256,597 titled “Token Surveys and Privacy ControlTechniques” by Markus Jakobsson, and Stephen C. Gerber.

Gathering User Data Via Wallet Applications

In many embodiments, a wallet is combined with or interfaces with anapplication that manages or is used for traditional credit cardtransactions. This may be a browser, a browser plugin, or aspecial-purpose application, including one that is supported by nativehardware functionality for purposes of security. The transactionsrecorded by this application can be stored by the wallet, as part of theuser profile. From this, anonymized profiles can be made, e.g., aprofile that matches a template requiring the purchase of airplanetickets, which may have been performed using the application. The dateof the travel may be information requested by the template, as may thelocation of the journey. An advertiser may tailor promotional content tothe destination of the travel. The cost of the placement of thepromotional material may be determined based on how long it is until thetrip will commence. A trip that has already concluded may be of lesservalue to some advertisers, but higher value to others; therefore, asadvertisers bid for the right to have their promotional contenttransmitted to the user, these types of data may be used as input. Ascan readily be appreciated, the specific data gathered and theapplications from which wallets gather data for incorporation withinuser profiles is largely only limited by the requirements of specificapplications.

While various processes are described above for using end-user modulesto gather user data, user data can also be gathered by mechanismsembedded within the operating systems and/or execution environments ofuser devices. Recommendation platforms that utilize executionenvironments to gather user data and/or generate recommendations inaccordance with various embodiments of the invention are discussedfurther below.

Recommendation Platform Execution Environments that Gather User Data

Recommendation platforms in accordance with several embodiments of theinvention utilize execution environments (EE) that identify end-userneeds in order to select meaningful promotional material, where theseneeds are determined based on end-user actions taken in the executionenvironment. The actions need not be considered in isolation. Forexample, a person looking at homes for sale may simply be interested inhome decoration ideas, whereas a person looking at homes for sale, andreviewing terms of mortgages is a potential home-buyer. This aggregationof data is not possible in environments where one vendor (such as arealtor placing one advertisement) may not have any way of exchangingdata with another vendor (such as a mortgage broker), but is madepossible by our approach. After the EE determines a need, it obtains andrenders associated material, which may include promotional material. Inthe example above, a first need may relate to a home of a certain pricerange in a certain geographic area. After the EE determines anotherassociated need, such as a mortgage of a related amount, it candetermine intent to a greater extent than it would have withoutdetermining the second need. This intent makes recognizing the actionsof the end-user more valuable, and increases the potential value ofpromoting content to the user. The EE further determines user responsesto rendered material to fine-tune the model, such as the apparentattention the user exhibits, the amount of time the user spends on thepromotional material, and more. This is also an indicator of intent, aswell as a useful observation to determine what types of promotionalmaterial a user may be interested in next. Thus, the EE builds a modelof the user and her needs, based on observation of actions, attention,and determinations of intent, among other things. Attention can bedetermined based on an array of approaches, disclosed herein.

An execution environment (EE) that observes and processes events inaccordance with an embodiment of the invention is illustrated in theFIG. 46 . The EE 4600 includes an event handler 4611 that observes orprocesses events, such as user requests, user actions, and contenttypes. The event handler writes the profile storage (details) 4601,which includes records related to events. In several embodiments, the EEincludes an interest identifier 4612 that determines user interestsbased on events detected by an event handler 4611, or obtainedexternally, e.g., from other associated EE units.

By way of example, the interest identifier 4612 may determine that auser associated with EE 4600 is interested in trucks costing between $50k-$60 k, and that the user is likely to have available funds for such apurchase. The interest identifier may also determine what percentage ofdetermined events that result in a conversion, such as a purchase, byobtaining information from the conversion identifier 4613. Thispercentage provides an indication of the likely strength of an interest.In a number of embodiments, the interest identifier may determine thatsome interest types have a greater conversion percentage than otherinterest types. For example, a user's identified interest related toclothing may have a higher conversion rate than a user's identifiedinterest related to consumer electronics. Such detailedcharacterizations help the interest identifier 4612 generate aprediction of likely conversion based on one or more events observed byevent handler 4611 or stored in profile storage (details) 4601. In oneembodiment, interest identifier uses machine learning (ML) methods ortime-series analysis to determine a predicted conversion probability forone or more types of merchandise and service related to one or moreevents. The interest identifier 4612 can generate entries including atype descriptor and a conversion probability, and stores such entries inthe profile storage (abbreviated) 4602. One such entry may include thepair ((“truck”, “$50 k-$60 k”, “Chevy”), 0.12). Here, the first elementof the pair, namely (“truck”, “$50 k-$60 k”, “Chevy”) describes aproduct, and the second element, namely 0.12, a conversion probability.The first element, in this example, corresponds to a truck of costbetween $50 k and $60 k, of the brand Chevy™. The interest identifiermay take as input an assessment indicating the attention measurement,such as whether the user observing a given advertisement was looking atthe advertisement, based on eye tracking technology, or whether the userturned off the volume during the advertisement. In certain embodiments,the conversion identifier 4613 may take external input, such as anindication that a coupon code associated with the user was used in apurchase, or an indication from event handler 4611 that a user performedan action indicating a conversion.

In a number of embodiments, the conversion unit 4614 determines whatpromotional and informational content is likely to be the reason for adetected conversion, where this may be (but is not limited to) expressedas scores. One advertisement that is determined to be likely to havecaused the conversion may be assigned a score of 67, out of a total of100, while three other advertisements that are determined likely to havecontributed as well are each given a score of 11, all these scoresadding up to 100. Attributions such as these are conveyed tomanufacturers, resellers and individual influencers, where one exampleinfluencer may be the party that created the content assigned 67 points.Scores and attribution data can be communicated by communication unit4615. This unit also performs backups and synchronizations of profilestorage (details) 4601 and profile storage (abbreviated) 4602. Thesestorages may be encrypted and/or authenticated prior to backup orsynchronization.

In a number of embodiments, the execution environment 4600 is part of aTEE, a DRM environment, or a DRM running in a TEE. While various EEenvironments are described above with respect to FIG. 46 , it should bereadily appreciated that any of a variety of EE can be utilized asappropriate to the requirements of specific applications in accordancewith various embodiments of the invention.

While various EEs are described above with reference to FIG. 46 , any ofa variety of EEs can be utilized as appropriate to the requirements ofspecific applications including (but not limited to) the various EEimplementations discussed below. Processes for enhancing informationcaptured by EEs related to actions by accounting for factors including(but not limited to) influence in accordance with various embodiments ofthe invention are discussed further below.

Accounting for Influence

EEs in accordance with many embodiments of the invention utilize anaccounting method used to determine what influences of relevance havetaken place, relative to a particular action or decision by a user. Forexample, the EE can determine that a user appears to be interested inbuying a truck, based on search terms entered by the user, newspaperarticles read by the user, attention paid by the user to variouspromotional content, and finally, actions that the user takes that canbe used to correlate to a later transaction. For example, a user mayaccept a coupon for a free window treatment for a truck the user may beinterested in buying, where the sale of the truck later, whether ittakes place online or in a brick-and-mortar setting, can be attributedto the coupon, and then, to one or more sessions observed by the EE. Thecoupon provided to the consumer may be offered by a first influencer. Ina traditional system, the understanding would have been that this is theparty that caused the sale of the truck, prompting the manufacturer toreward this first influencer for finding a customer. However, that maynot be what happened. The consumer may have gone to the site of thefirst influencer, knowing a coupon could be obtained there, after firsthaving been informed of the benefits of the truck by a secondinfluencer, which may be the party that actually caused the consumer todecide to buy the truck. To properly reward the efforts of influencers,the manufacturer would want to recognize the efforts of both the firstand the second influencers. By rewarding those whose actions actuallyinfluence decisions, manufacturers and sellers streamline the deliveryof promotional material and improve their chances of increasing sales.This is possible in the context of the disclosed system, since the EEwill record the actions of the user, and will know, for example, thatthe user received information from both the first and second influencer.The EE will also be able to determine likely inflection points, such asa time when a user contacts lenders or starts searching for accompanyingproducts, such as bug screens or flatbed covers. These inflection pointscan help identify when a user tendency or decision appears to have beenformed. Whereas it may not be possible to determine that a sale, such asof a truck, will take place just because one of these events isobserved, it will be possible to probabilistically and retroactivelydetermine likely inflection points after information of a sale, or otherconversion, is recorded. This enables the retroactive attribution ofwhat influencing efforts led to the conversion, which in turn enables amore precise form of rewarding of these efforts.

Attribution information can be communicated to an advertiser, amanufacturer, an influencer, or a retailer, for example. The advertiserand the influencer may wish to know the success of their promotionalcontent, to determine what efforts of theirs are most successful. Inseveral embodiments, an advertiser may also want to know what effortsled to success for purposes of tracking awards. An advertiser may haveproduced the content, or outsourced the production to an influencer. Ifthe latter, the advertiser can expect to collect an award from the brandthat contracted with the advertiser, but also, the advertiser would beexpected to pay the influencer. The brand that pays the advertiserand/or the influencer may correspond to a manufacturer, e.g., ofmattresses, or a retailer of any type of bed product, including severalbrands of mattresses. Brands and retailers may want to know whatcampaigns, advertisers, influencers and/or products are most successfulin terms of leading to conversions. Moreover, all the parties receivinginformation benefit from understanding demographics associated with theconversions, in order to better know their audience and their customers.Thus, these parties may receive a first information related to therendering of a promotional content element, where the first messageincludes some demographic information, such as the zip code orgeolocation of the viewer. Viewing content in full is a form ofconversion. They may also receive a second information related to thepurchase of a product advertised in the promotional content element, ora request for more information about the product. These are also typesof conversions. The second information may include additionaldemographic information, e.g., descriptors of the users such as thecontents of the profile storage (abbreviated), as illustrated below, aswell as location information. Profile data of this type can be one typeof demographic data. This second information could be the same as thefirst information in terms of the type of demographic data that istransmitted. Additional examples of content elements, includingpersonalizable content, and their use are provided in U.S. ProvisionalPatent Application No. 63/210,042 titled “Dynamic Distributed DigitalRights Management Enabling Composite Content” by Markus Jakobsson.

Restricting Access to Profile Data

Creating and maintaining a localized model of a user and her needs, asdescribed above, thus enables both greater insights into likely userneeds and likely inflection points. It also enables a localizedsafe-keeping of profile data, which protects the consumer againstbreaches as well as acts to instill trust with the consumer. However, itis desirable to protect this profile information against theft, e.g., bymalicious processes running on the computer system including the EE.

In several embodiments, access to profile data on an EE is limited. Thiscan be done by encrypting the profile data, and by managing, usingaccess control mechanisms, what processes are entitled to access theencrypted data. By protecting the profile data, multiple associated EEscan safely consolidate profile data. For example, one user may use alaptop during the day and a tablet computer at night. The EE associatedwith the laptop and the EE associated with the tablet computer can,after determining that they are associated with the same user, exchangeinformation with each other, thereby enabling improved insights, whetherinto consumer needs or attributions related to conversions. It isbeneficial for such data also to be hosted on cloud servers, in order tofacilitate a near-instantaneous personalization of a new devicepurchased by the user, or temporarily configure an EE of a new device,such as a hotel TV, to select suitable promotional content and tocontribute insights to the cloud-hosted profile of the user. As canreadily be appreciated, similar mechanisms can be used to synchronizedwith any of a variety of devices associated with the consumer, such asthe consumer's phone or laptop. In several embodiments, synchronizationinvolves a binding of an EE to a cloud profile of a user, whethertemporarily or for a longer period of time; this binding can be informedby a user log-in, whether this is based on an identifier and a password,an identifier and a biometric, or an identifier and another form ofcredential. For temporary bindings, a user may not be comfortable orsufficiently incentivized to enter a password or biometric into atemporary device, such as a hotel TV. However, users may be willing toutilize their phone as an authentication platform, which then conveys anencoded identifier to the temporary platform, which in turn uses this tobind to an associated cloud profile. Automated bindings may also occurbased upon Bluetooth beacons, etc. As can readily be appreciated any ofa variety of mechanisms can be used for binding as appropriate to therequirements of specific applications. Binding in this way may onlyenable the uploading of profile data, from the temporary platform to thecloud service, or it may also enable sharing of information in the otherdirection. The extent of this sharing may be configured by the user, onhis phone for example, where one configuration for temporary devices maybe used time after time without any need for user effort.

A process for synchronization of profile storage (details) 4601 andprofile storage (abbreviated) 4602 associated with an EE in accordancewith an embodiment of the invention is illustrated in FIG. 47 . Theprofile storage (details) 4601 and profile storage (abbreviated) 4602,both which are associated with EE (not shown) are hosted on long-termdevice 4700, which may be (but is not limited to) a laptop, desktop,cellular phone, tablet computer, home remote control or home TV.

Updates to profile storage (details) 4601 can be encrypted (not shown)and transmitted to storage A 4711, which is part of storage service4710, and which may be a cloud storage service. Alternatively, iflong-term device 4700 is a master device that performs aggregation ofprofile storage (details) 4601, long-term device 4700 does notcommunicate updates to profile storage (details) 4601, but insteadperiodically transmits the entire profile storage (details) 4601, inprotected format, to storage A 4711. The protection may includeencryption and authentication. By doing this, multiple additions made byone or more devices are aggregated into the same ciphertext elementinstead of multiple independent ciphertext elements. The contents ofstorage A 4711 can be synchronized with long-term device 4702 associatedwith the same user as long-term device 4700, but not copied toshort-term device 4701 unless the user associated with the dataspecifically requests this. Long-term device 4700, and associated EE,periodically generates the updated contents of profile storage(abbreviated) 4602 from profile storage (details) 4601, where profilestorage (abbreviated) 4602 includes high-level characterizations of userpreferences, and profile storage (details) 4601 includes detailedevent-based characterizations. Long-term device 4700 periodicallyupdates the contents of storage B 4712 with the contents of profilestorage (abbreviated) 4602, which are synchronized with short-termdevice 4701 as this is being associated with the user associated withprofile storage (details) 4601 and profile storage (abbreviated) 4602.This may take place, for example, as a user of personal device 4703 usespersonal device 4703 to authenticate to short-term device 4701 in amanner that is associated with an identifier linked to storage B 4712.This link may be expressed as a public key, whether short-term orstatic, and the authentication may include a digital signature that isverified using the public key.

A content token with associated data implemented in accordance with anembodiment of the invention is illustrated in FIG. 48 . The contenttoken 4800 includes a first content element 4801 and a second contentelement 4802. For example, the first content element 4801 may be amovie, and second content element 4802 the sound track or subtitles forthe movie. Alternatively, the first content element 4801 may be a movieor a song, and second content element 4802 a reference that, whenresolved, causes the selection of a commercial to be rendered along withor interspersed in the content of the first content element 4801.

In the illustrated embodiment, the descriptor 4803 includes one of moreclassifications of first content element 4801 and/or second contentelement 4802, where example classifications may include labels such as“pre-teen”, “horses”, “princess”, and associations with productcategories that may be of interest to viewers, where these productcategories may be identified with references or labels such as “animedolls”. The terms 4804 specify the conditions that need to be satisfiedfor the content of the content token 4800 to be rendered, played orused; example terms may specify geographic boundaries where it is or isnot allowed; DRM descriptors identifying required DRM standards forprocessing the content; TEE descriptors identifying allowed platforms toplay the content; subscription identifiers specifying what membershipsor payments are needed to use the content, or to provide a discount toaccess the content.

In certain embodiments, policies 4805 specify the conditions of usage,e.g., only allowed for accounts that are enabled for PG-13 usage,payment policies, and more. In a number of embodiments, first originatorinformation 4806 may identify one or more originators associated withthe first content element 4801, and second originator information 4807may identify one or more originators associated with the second contentelement 4802. In several embodiments, the content-specific identifier4808 is a unique identifier associated with content token 4800.

In one embodiment, the EE is running on a trusted execution environment(TEE) or a Digital Rights Management (DRM) supported platform, which mayin turn be running in a TEE. The TEE and DRM platform are also used forpurposes of delivering content to users in a secure manner, and inaccordance with licenses associated with the content. By using the sameTEE and DRM of the current disclosure, additional benefits are obtained.For example, as content is delivered, the type of content informs the EEof the interests and needs of the user. The more the EE knows, thebetter recommendations for content and promotional content it canprovide, and the better guidance. As one example of guidance isinformation of applicable offers related to users of the identifiedprofile, where such offers can provide reductions of costs, such as ofloans, and suggestions of potentially interesting vacation locations anddeals on travel there. Another example of guidance is informationadvising the user of unnecessary services, exorbitant costs, phishingattacks, and potentially illegal activity. In contrast to existingtargeted advertising platforms, which are not providing content to helpthe user, but to maximize the profit obtained from the user, thedisclosed technologies both address the needs of users and of serviceproviders wishing to promote their merchandise and services. Whenpromotions are more successful due to more appropriate suggestions, asmaller number of promotional items are needed to pay for servicesprovided to the user. For example, instead of displaying fourcommercials every ten minutes during a movie watched by the user, thesystem may identify one or two promotional content elements to displayduring the entire movie. This is less disruptive and more informative tothe user, while at the same time creating greater benefits to theadvertiser.

While specific EEs, processes for synchronizing data between EEs and/orprocesses that can be utilized by EEs to monitor interactions withcontent tokens are described, any of a variety of EE configurations andprocesses for building profile data using EEs can be utilized asappropriate to the requirements of specific applications in accordancewith various embodiments of the invention. Processes for securelyappending data and verifying authorization to access appended data inaccordance with various embodiments of the invention are discussedfurther below.

Appending Information to Records

In several embodiments, a cloud platform has two storage areasassociated with each user identifier: a first and encrypted area that isnot accessible by the cloud platform, and a second area that storesinformation that is accessible to the cloud platform. The first area canstore profile information such as the information described above, whichmay have considerable amounts of details, some of which may bepersonally identifiable or sensitive. The second area may includegeneral topics of information that is of interest to the user, but nopersonal information and/or not sensitive information. For example, thesecond area may include information specifying that the associated useris interested in new trucks in the price range of $50-$65 k. An EE of atemporary device may be allowed to contribute to the first area, but notaccess data from it. The first area can be referred to as “append-only”with respect to the temporary device. However, the EE of the temporarydevice may be provided with information from the second area.Alternatively, after binding to the cloud platform and providing a useridentifier, the EE of the temporary device may be provided withpromotional material, or references to such material, from the cloudplatform, where the selections can be informed by the contents of thesecond area. These contents can be generated by EEs with access to thecontent of the first area, such as an EE associated with the user'sphone.

In several embodiments, append-only areas can be created by use of apublic key associated with the user's EE. In a number of embodiments,the associated private key is not known by the cloud platform or byservice providers. The EE of temporary devices associated with the usercan have access to the public key, but not the associated private key.In certain embodiments, the EE of these temporary devices can appendinformation to the records in the append-only area by selecting asession key, which can be a symmetric key, and encrypting the symmetrickey with the public key associated with the EE of the user. In certainembodiments, the EE uses the symmetric key to encrypt data to be addedto the append-only area. Such data can be authenticated using a messageauthentication code and/or a digital signature. The messageauthentication code may use the same symmetric key as for the encryptionof the data, or another symmetric key that is encrypted with the publickey of the EE of the user. A digital signature may be generated using aprivate key corresponding to a public key of the EE of the temporarydevice. The data of the append-only area can be stored in a traditionaldatabase and/or using distributed methods. Distributed methods thatinclude the use of error-correction and fragmenting of error correcteddata, are described in U.S. Provisional Patent Application No.63/232,728 titled “Secure and Intelligent Decentralized Technology” byMarkus Jakobsson, Stephen C. Gerber and Ajay Kapur. As can be readilyappreciated any of a variety of mechanisms can be utilized for securelyappending and authenticating access to appended data in accordance withvarious embodiments of the invention.

An EE that implements append-only functionality in accordance with anembodiment of the invention is conceptually illustrated in FIG. 49 . Inthe illustrated embodiment, an append-only area 4900 is illustrated thatcan only be rewritten by an authorized entity. At a first time, theappend-only area 4900 includes a first encrypted element 4901, a secondencrypted element 4902, and a log 4903 that describes when and by whatentity the first encrypted element 4901 and the second encrypted element4902 were added to append-only area 4900, as well as optionalauthentication values, e.g., of the entities that appended firstencrypted element 4901 and second encrypted element 4902 to append-onlyarea 4900. As can readily be appreciated, the specific information thatis written to an append-only area is largely determined by therequirements of a specific application.

In certain embodiments, the first encrypted element 4901 and the secondencrypted element 4902 include authentication values. Furthermore, thelog 4903 may be encrypted. There may also be more than two encryptedelements similar to the first encrypted element 4901 and secondencrypted element 4902.

At a later time, an updated append-only area 4910 can be created fromappend-only area 4900 by an entity with access to the decryption key(s)used to encrypt the first encrypted element 4901, and the secondencrypted element 4902. In certain embodiment, the decryption key(s) maybe one or more private keys associated with a public key(s) associatedwith append-only area 4900 as well as updated append-only area 4910.

An entity with access to the decryption key(s) can decrypt the firstencrypted element 4901 and the second encrypted element 4902. In certainembodiments, an EE determines, based on log 4903 how to combine theresults of the now-decrypted first element and now-decrypted secondelement. From these, the EE can generate an aggregated encrypted element4911. Furthermore, an entity can create an updated log 4912 based on thelog 4903 form the append-only area and additional actions performed bythe entity as it created aggregated encrypted element 4911 from thefirst encrypted element 4901 and then second encrypted element 4902. Theentity can then replace the append-only area 4900 with the updatedappend-only area 4910.

In a number of embodiments, the second area of storage described abovecan be implemented using storage of the same types as the first storagearea. Alternatively, and because the amounts of data associated with thesecond area of storage are much more limited than those of the firstarea, it can also be stored on a blockchain in some instances. Inseveral embodiments, the data of the second area of storage can bestored in a public database, and in a manner that enables access by anyroutine. To avoid negative impacts on user privacy, the records in thissecond area would not be associated with a long-term identifier of theuser to which they belong, but instead using a short-lived pseudonymthat is generated by the EE of the user, e.g., using a rolling codealgorithm. In many embodiments, a pseudonym is valid for a period oftime (e.g., a week), and then the associated record is not relevant, anda new entry is created, describing potentially new interests and a newpseudonym identifier that is not correlated by a third party to thecurrent pseudonym. For example, a pseudonym may be generated ashash(seed, i), where seed is a user-specific random value stored by theuser's EE, but which is not accessible to the human user. Here, i is acounter that identifies a time period, and hash a cryptographic hashfunction or other one-way function. It can also be generated as a newpublic key, which corresponds to a private key that is generated ashash(seed, i). In this example use case, a phone or other device used bythe user to provide information to a temporary EE would not convey along-term identifier to the temporary EE, but instead, the time-limitedpseudonym. When the EE of the temporary device writes data to theappend-only area, the temporary key can be used to encrypt the data. Thelong-term EE associated with the user can have access to thecorresponding private key, and is able to combine different append-onlyadditions with the other data of the record, generating an updatedrecord to be stored in the first area.

A process for utilizing pseudonyms to securely access profile data inaccordance with an embodiment of the invention is conceptuallyillustrated FIG. 51 . In the illustrate embodiment, two consecutiveelements of profile storage (abbreviated) 5100 a and 5100 b withassociated pseudonyms are shown. The profile storage (abbreviated) 5100a includes a first profile descriptor 5101, such as (but not limited to)“Interested in buying Ford Bronco, 1990-1995”. In several embodiments,the profile storage (abbreviated) 5100 a can also include a firstpseudonym 5102, which may be (but is not limited to) a 256-bit binaryvalue generated using SHA256 on a key and a counter, where the key isknown only by the party generating the first pseudonym 5102, which maybe the same entity that generated an updated append-only area from anappend-only area. Profile storage (abbreviated) 5100 a can have anexpiration date (not shown) which may be the same as the expiration dateof many other profile storage elements related to other users. At thetime of the expiration, updated profile storage (abbreviated) 5100 b isused instead of profile storage (abbreviated) 5100 a. For example, whena temporary EE requests information about a user's interest, the userwould be associated with a value matching first pseudonym 5102 during afirst time period, and with a value matching second pseudonym 5103during a second time period that follows the first time period. In someinstances, the second profile descriptor 5103 may state a differentinterest than the first profile descriptor 5101, and may state (but isnot limited to) “Interested in seat heater for trucks” or “Has recentlypurchased a 1992 Ford Bronco”, where the latter may indicate a potentialinterest for related products or services and be based on informationthat the user performed a purchase.

While specific processes for utilizing pseudonyms to enable temporaryEEs to securely access profile data are described above with referenceto FIG. 51 , any of a variety of processes can be utilized to secureprofile data accumulated by EEs in accordance with various embodimentsof the invention. Ways in which multiple EEs may interact to gather useraction information in accordance with certain embodiments of theinvention are discussed further below.

Tracking Users Using Multiple Execution Environments

In several embodiments, two or more execution environments may co-existin a similar manner as to the dual-storage solutions described above.The two or more execution environments can split the duty of trackinguser and influencer behaviors with private, or personally identifiableinformation maintained by just one of the two instances. This split dutycan enable one execution environment to interact with advertisers, forinstance, without personally identifiable information. Such interactioncan provide advertisers with generic information about interest in theirproduct, such as when the number of non-private EE instances reportinginterest in a given product increases. Advertisers often promoteproducts and services in geographical regions, such as countries,counties, cities, and zip codes. Users will be more willing toparticipate in EE monitoring of their behaviors if they believe aprivacy firewall exists between the two EEs that may be monitoring theiractions.

In a simple instantiation of dual EEs, a first EE monitors actions andpersonal information of a specific individual, group, or organization.The first EE can provide the second EE with generic action data that isnon-specific to the specific individual, group, or organization. The twoEEs may be co-located, or they may be far apart, such as in differentcountries. The EE instantiations may regularly shift their processinglocations in an effort to balance processing loads and to reducelocation-based privacy incursions. The EE containing non-personallyidentifiable information may support multiple EE instances whereby asingle EE instance can communicate and aggregate non-personallyidentifiable information from one or more EE instances containingprivate information. For example, a manufacturer of bedroom mattressesmay wish to improve store sales in a particular zip code with a targetedand highly efficient promotional effort. The manufacturer may utilize aninfluencer or recommender approach of promotion whereby the manufactureridentifies recent buyers of their product in the target zip code throughsales data or registrations, pushes a request for product review viaemail to those buyers, and then utilizes those reviews with targetedbrowser advertisements in IP ranges known to exist within or near thetarget zip code. In exchange for the review, the manufacturer can offerthe reviewer a discounted product (e.g., a pillow). As can readily beappreciated, the specific manner in which multiple EEs within arecommendation platform can present recommendations and/or coordinatewith advertisers to present advertisements to users is only limited bythe requirements of specific applications.

In certain embodiments, advertisements (e.g., display advertisementsviewable within a web browser application) may include monitoringcapabilities to determine clicks-throughs to the individual buyerreviews. Reviews that place the manufacturer and product in a positivemanner should receive, on average, more conversions. In certainembodiments, an advertisement can interact with a first EE, where apotential buyer's personal information is known, and may be configuredwith EE monitoring characteristics that are controlled by the potentialbuyer. The first EE, detecting a click-through to a review, cancommunicate to a second EE the potential buyer's click-through, the oneor more reviews displayed, and the zip code of the potential buyer. Thefirst EE may be configured to monitor the potential buyer's email orpurchase history to later confirm with certainty that the potentialbuyer made a purchase at the store, or online, and is in the targetedzip code. This purchase confirmation may be provided again to the secondEE, but again only with product information and generic zip codedetails. The successful purchase attribution may now be made to theoriginal buyer and their review, and a pillow discount offered. Theoriginal buyer may also be interacting with a dual-EE system whereby theoriginal buyer is also treated with privacy and provided the discount ina reverse direction from the original buyer's second EE to their firstEE. The advertisement referenced in the example above, may be replacedwith a personalized animated character that speaks to the potentialbuyer through augmented and/or virtual environments such as interactionswith a smartphone voice assistant or an animated character in a gamingenvironment. In the animated character example, the EE system wouldinteract with the potential buyer, and/or the previous buyer, in amanner similar to that of the browser advertisement example. As can bereadily appreciated, EEs implemented in accordance with variousembodiments of the invention are not limited to interact with specificadvertising channels and can track conversions with respect to any of avariety of advertising platforms appropriate to the requirements of aspecific application.

A process for determining an attribution associated with a conversion inaccordance with an embodiment of the invention is illustrated in FIG. 50. The process 5000 includes an EE logging (5001) a first event, whichmay be determined by event handler. In several embodiments, the EE alsologs (5002) user actions. Examples of user actions can include (but arenot limited to) time spent watching an advertisement before skipping it,if applicable.

In certain embodiments, the EE logs (5003) a second event 503, which mayalso be determined by event handler, but may also be determined by anexternal entity and conveyed to the EE. The EE can also log (5004) useractions related to the second event. In many embodiments, the EE obtains(5005) a conversion indication. For example, conversion indications caninclude (but are not limited to) a user action observed by eventhandler, such as a purchase request or request for a coupon. They mayalso be external, such as a message being conveyed to the EE statingthat a particular coupon associated with the logged second event (5003),for example, was used.

In many embodiments, the EE determines (5006) an attribution of theconversion to one or more events, such as the first event and the secondevent. In several embodiments, the attribution can be determined byanalyzing the logs and determining, potentially using a rule-basedengine or a machine learning based model or AI model. The attributioncan be conveyed (5007 to an external entity, such as (but not limitedto) an entity that provides awards to product promoters based on theirapparent success in causing conversions such as purchases.

FIG. 52 illustrates an advertiser utilizing a dual execution environmentconfiguration to influence a potential buyer. The advertiser 5201, whichcan be considered to have a similar role to mattress advertiser in theexample discussed above, utilizes retail store sales data 5202 andadvertisements 5209 to influence a potential buyer 5208 with the use ofa pair of dual EE configurations. The first dual EE configuration caninvolve a previous buyer EE A 5203 and a previous buyer EE B 5204. Thesecond dual EE configuration involves a potential buyer EE A 5106 and apotential buyer EE B 5207. Advertiser 5201 can utilize retail store data5202 to offer an enticement such as (but not limited to) an enticementfor a coupon to leave a review. The enticement may be through anysuitable contact method, including the ability for the advertiser 5201to contact previous buyer 5205 through an anonymous dual EE. Theadvertiser receives a publicly viewable review from previous buyer 5205,advertises 5209 the review publicly, or in a targeted manner, such asgeographical, and entices potential buyer 5208 to click through theadvertisement and ultimately purchase the mattress. The click-throughcan pass through the potential buyer EE pair and the influencer. In manyembodiments, the previous buyer 5205 is credited with influencing a saleand ultimately receives a benefit (e.g., a coupon). One skilled in theart will recognize that the advertiser and buyers have numerouspotential communication mediums with which to pass information,including an EE, or a pair of EEs designed to protect consumer privacyand encourage the consumer to adopt the new form of communication andtracking. Accordingly, a variety of processes can be utilized by EEs todetermine attribution in accordance with various embodiments of theinvention.

The described execution environments and associated storage systems maybe operated on behalf of promoters or advertisers, they may be operatedon behalf of a specific individual, group, or organization, or acombination of both. EE systems operated solely by advertisers may beinherently considered untrustworthy by consumers. Consumers generallyprefer to not be spied on, even if the advertiser is utilizing a dual EEenvironment to assure the consumer's privacy. Therefore, it is expectedthat the disclosed EE systems may be configured on behalf of specificconsumers, or at their request in exchange for value, such as theability to receive recommendations, rewards, coupons, deal suggestions,etc. An EE promotional system configured with the full consent of theconsumer is likely to improve the adoption of such systems and toincrease the trust between advertisers and consumers.

While various EEs are described above, it should be readily beappreciated that the processes similar to those described above can beperformed on a variety of EEs. In several embodiments, the EE mayinclude an operating system running a DRM, with the support of a TEE,such as TrustZone. The DRM may, at configuration time, generate a keypair, including a public key and a private key, and become associatedwith the public key. One way to associate the EE may be for a trustedthird party to generate a digital certificate on the public key, and ona descriptor of the EE. This descriptor may include a unique identifier,and a list of descriptive elements related to the DRM model, the type ofTEE, the type and minimum version of the operating system. The minimumversion corresponds to the installed version at the time ofcertification. As this at some point is updated to a newer version ofthe same operating system, it remains in compliance with thecertificate. Alternatively, the certificate may require updating once anewer version of the operating system is available, to verify andrecertify that it runs the more recent version. In some instances, acertified EE that is in compliance with requirements is trusted torequest an update of the certificate without on-site inspection, withthe certificate authority simply reviewing statements made from a unitthat is part of the DRM running in the EE, and determining based onthese statements whether to recertify. One such statement may be theresult of an anti-virus scan; another may be a list of all installedsoftware and its versions. Based on the extent to which an EE satisfiesrequirements verified by the certificate authority, it may qualify fordifferent levels of certification. Based on the level of certification,different types of computation may be allowed to be performed by the EE,different types of inputs may be provided to the EE, and/or differenttypes of roles are allowed by the EE. One example of a role is toperform actions associated with trusted tokens, as disclosed in U.S.Provisional Patent Application No. 63/213,251 titled “Token Creation andManagement Structure”, by Markus Jakobsson. Some actions, such asprocessing data using secret keys, such as described in “Integration ofReal-World Data and Measurements into Distributed and Consensus-BasedEnvironments” by Markus Jakobsson, may require a minimum DRM requirementto be met. Yet other actions, such as storing sensitive data, orportions of such data, may require the use of a TEE that meets someminimum requirements. This is disclosed in U.S. Provisional PatentApplication No. 63/232,728 titled “Secure and Intelligent DecentralizedTechnology” by Markus Jakobsson, Stephen C. Gerber, and Ajay Kapur.Having a minimum certification level may also be required to receiveaccess to sensitive profile data, such as the detailed first storagearea profile data disclosed herein. Other security requirements placedon executional environments may include (but are not limited to) havingan approved anti-virus software module installed, having an approvedoperating system installed, having an approved browser type and versioninstalled, having all patches of an identified type installed, not beingassociated with high-risk IP addresses, and/or communicating between EEswith a secure virtual private network (VPN).

Exemplary Embodiments

While many embodiments are described above, a number of significantexemplary embodiments of the various inventions described herein areprovided below by way of example.

A recommendation platform in accordance with a first embodimentincludes: a network interface; memory; and a processor, the processorconfigured to:

retrieve transaction data records stored within an immutable ledger,where each retrieved data record is associated with a public key; and

each public key is associated with a user profile;

identify a need associated with a specific user profile based uponinformation including retrieved data records associated with the publickey of the specific user profile;

identify retrieved data records containing information relevant to theidentified need;

identify user profiles associated with the retrieved data recordscontaining information relevant to the identified need;

determine weights based upon similarity of the identified user profilesassociated with the retrieved data records containing informationrelevant to the identified need and the specific user profile;

weight the information from the retrieved data records containinginformation relevant to the identified need based upon the determinedweights;

generate a recommendation with respect to a resource based upon theweighted information from the retrieved data records containinginformation relevant to the identified need; and

transmit the recommendation to a user device associated with thespecific user profile.

A recommendation platform in accordance with a second embodimentincludes the features of the first embodiment, wherein the resource isat least one of a specific product and a specific service.

A recommendation platform in accordance with a third embodiment includesthe features of any of the first and second embodiments, wherein theretrieved transaction data records stored within an immutable ledgerinclude transactions reflecting actions associated with user profiles.

A recommendation platform in accordance with a fourth embodimentincludes the features of any of the first through third embodiments,wherein the processor is further configured to: determine a precision ofthe recommendation based upon the weighted information from theretrieved data records containing information relevant to the identifiedneed; and transmit the precision to the user device associated with thespecific user profile.

A recommendation platform in accordance with a fifth embodiment includesthe features of the fourth embodiment, wherein the processor is furtherconfigured to determine that the precision is insufficient to generate arecommendation.

A recommendation platform in accordance with a sixth embodiment includesthe features of the fifth embodiment, wherein the determination that theprecision is insufficient to generate a recommendation includescomparing the determined precision to a threshold.

A recommendation platform in accordance with a seventh embodimentincludes the features of any of the first through sixth embodiments,wherein the recommendation includes rendering information that directsthe user device how to render the recommendation via a user interface.

A recommendation platform in accordance with an eighth embodimentincludes the features of any of the first through seventh embodiments,wherein the recommendation includes a plurality of alternativerecommendations.

A recommendation platform in accordance with a ninth embodiment includesthe features of any of the first through eighth embodiments, wherein theprocessor is further configured to determine weights by generating amatrix of weights for the specific user profile.

A recommendation platform in accordance with a tenth embodiment includesthe features of any of the first through ninth embodiments, wherein thematrix of weights includes: a first dimension that corresponds todifferent user profiles; and a second dimension that corresponds to aweighted array, where each entry in the weighted array corresponds to aweight for one resource characterized in that the weight indicates thesimilarity of the specific user profile with one of the different userprofiles.

A recommendation platform in accordance with an eleventh embodimentincludes the features of the tenth embodiment, wherein the matrix ofweights further includes a third dimension corresponding to differentresources.

A recommendation platform in accordance with a twelfth embodimentincludes the features of any of the tenth or eleventh embodiments,wherein the matrix of weights further includes at least one dimensionrepresenting at least one of temporal aspects, and preferences.

A recommendation platform in accordance with a thirteenth embodimentincludes the features of any of the first through twelfth embodiments,wherein the processor is further configured to estimate the scarcity ofa resource to which the recommendation relates.

A recommendation platform in accordance with a fourteenth embodimentincludes the features of any of the first through thirteenthembodiments, wherein the processor is further configured to receive thespecific user profile as an input.

A recommendation platform in accordance with a fifteenth embodimentincludes the features of the fourteenth embodiment, wherein the specificuser profile includes at least one selected from the group consistingof:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

A recommendation platform in accordance with a sixteenth embodimentincludes the features of any of the first through fifteenth embodiments,wherein the processor is further configured to identify opinionsassociated with the specific user profile.

A recommendation platform in accordance with a seventeenth embodimentincludes the features of the sixteenth embodiment, wherein at least oneof the opinions associated with the specific user profile is identifiedwithin the retrieved transaction data records.

A recommendation platform in accordance with an eighteenth embodimentincludes the features of any of the first through seventeenthembodiments, wherein the processor is further configured to use thespecific user profile to retrieve at least one additional source ofinformation selected from the group consisting of:

demographic data;

action data; and

at least one recommendation associated with the specific user profile.

A recommendation platform in accordance with a nineteenth embodimentincludes the features of any of the first through eighteenthembodiments, wherein the processor is further configured to storeinformation concerning features of the resource as an N-dimensionalfeature vector.

A recommendation platform in accordance with a twentieth embodimentincludes the features of any of the first through nineteenthembodiments, wherein the processor is further configured to generate aself-organized map of a plurality of resources based upon N-dimensionalfeature vectors for each of the plurality of resources.

A recommendation platform in accordance with a twenty-first embodimentincludes the features of any of the first through twentieth embodiments,wherein the processor is further configured to generate a lowerdimensional representation of a plurality of resources based uponN-dimensional feature vectors for each of the plurality of resources.

A recommendation platform in accordance with a twenty-second embodimentincludes the features of any of the first through twenty-firstembodiments, wherein the processor is further configured to identify asubspace of a plurality of resources based upon N-dimensional featurevectors for each of the plurality of resources.

A recommendation platform in accordance with a twenty-third embodimentincludes the features of any of the first through twenty-secondembodiments, wherein the subspace corresponds to a plurality ofresources having at least one characteristic relevant to the identifiedneed.

A recommendation platform in accordance with a twenty-fourth embodimentincludes the features of any of the first through twenty-thirdembodiments, wherein the processor is further configured to detect userprofiles associated with abuse based upon the retrieve transaction datarecords and a representation of a plurality of resources.

A recommendation platform in accordance with a twenty-fifth embodimentincludes the features of any of the first through twenty-fourthembodiments, wherein the processor is further configured toautomatically generate user profiles.

A recommendation platform in accordance with a twenty-sixth embodimentincludes the features of any of the first through twenty-fifthembodiments, wherein the retrieved transaction data records includeinformation concerning conversion of a previously generatedrecommendation and the processor is further configured to determine anattribution for the conversion to one of the user profiles associatedwith the retrieved data records.

A process in accordance with a twenty-seventh embodiment including:

retrieving data records from at least one immutable ledger using arecommendation platform, where each retrieved data record is associatedwith a public key;

associating information obtained from retrieved data records associatedwith specific public keys using the recommendation platform;

generating at least one user profile based on information associatedwith specific public keys using the recommendation platform;

generating at least one characterization for association within the atleast one user profile based on information associated with specificpublic keys using the recommendation platform;

receiving a request with respect to an identifier associated with anidentified user profile using the recommendation platform; and

generating a response to the request based upon at least onecharacterization associated with the identified user profile using therecommendation platform.

A process in accordance with a twenty-eight embodiment includes thefeatures of the twenty-seventh embodiment and further including creatinga cluster of user profiles based upon characterizations associated withthe clustered user profiles.

A process in accordance with a twenty-ninth embodiment includes thefeatures of the twenty-eight embodiments, wherein the response is arecommendation generated based upon the cluster of user profiles.

A process in accordance with a thirtieth embodiment includes thefeatures of any of the twenty-seventh to twenty-ninth embodiments,wherein the request includes a template identifying at least onecharacterization.

A process in accordance with a thirty-first embodiment includes thefeatures of any of the twenty-seventh and thirtieth embodiments andfurther including receiving information associated with the at least oneuser profile from at least one end-user module present on at least oneuser device.

A process in accordance with a thirty-second embodiment includes thefeatures of the thirty-first embodiment, wherein the informationassociated with the at least one user profile is a token.

A process in accordance with a thirty-third embodiment includes thefeatures of any of the twenty-seventh to thirty-second embodiments,wherein the response is a token.

A process in accordance with a thirty-fourth embodiment includes thefeatures of any of the twenty-seventh to thirty-third embodiments andfurther including receiving information associated with the at least oneuser profile from at least one execution environment present on at leastone user devices.

A user device in accordance with a thirty-fifth embodiment includes: anetwork interface; memory; and a processor, the processor configured toimplement an execution environment that enables:

-   -   initiation of transactions via an immutable ledger;    -   recordation of events;    -   updating a user profile, where the user profile includes at        least one characterization associated with the user profile;    -   encrypting the updated user profile and securely storing the        encrypted user profile;    -   receiving a request to access the encrypted user profile from a        process;    -   determining access permissions of the process; and    -   when the process has sufficient access permissions, decrypting        the user profile and providing user profile data to the process.

A user device in accordance with a thirty-sixth embodiment includes thefeatures of the thirty-fifth embodiment, wherein the events include userrequest and user actions.

A user device in accordance with a thirty-seventh embodiment includesthe features any of the thirty-fifth and thirty-sixth embodiments,wherein the processor is further configured to transmit user profiledata for remote storage.

A user device in accordance with a thirty-eighth embodiment includes thefeatures any of the thirty-fifth to thirty-seventh embodiments, whereinthe processor is further configured to receive user profile data from aremote source and the execution environment enables updating of the userprofile data of the encrypted user profile based upon the received userprofile data.

A user device in accordance with a thirty-ninth embodiment includes thefeatures any of the thirty-fifth to thirty-eighth embodiments, whereinthe processor is further configured to create an append-only area ofuser profile data using a public key associated with the executionenvironment and a corresponding private key.

A user device in accordance with a fortieth embodiment includes thefeatures any of the thirty-fifth to thirty-ninth embodiments, whereinthe request includes a pseudonym and the process is determined to havesufficient access permissions when the pseudonym is matches a pseudonymassociated with the user profile.

A recommendation platform in accordance with a forty-first embodimentincluding: a network interface; memory; and a processor, the processorconfigured to:

-   -   retrieve transaction data records stored within an immutable        ledger, where each retrieved data record is associated with a        public key; and    -   each public key is associated with a user profile;    -   identify a plurality of needs;    -   identify retrieved data records containing information relevant        to each of the plurality of identified needs;    -   identify user profiles associated with the retrieved data        records containing information relevant to the identified need;    -   determine a plurality of recommendations with respect to each of        the plurality of identified needs based upon the retrieved data        records containing information relevant to each of the plurality        of identified needs;    -   identify a need from the plurality of identified needs        associated with a specific user profile based upon information        including retrieved data records associated with the public key        of the specific user profile;    -   determine weights based upon similarity of the identified user        profiles associated with the retrieved data records containing        information relevant to the identified need from the plurality        of identified needs and the specific user profile;    -   weight the plurality of recommendations determined with respect        to identified need from the plurality of identified needs based        upon the determined weights;    -   generate a recommendation based upon the plurality of weighted        recommendations; and    -   transmit the recommendation to a user device associated with the        specific user profile.

A recommendation platform in accordance with a forty-second embodimentincludes the features of the forty-first embodiment, wherein theresource is at least one of a specific product and a specific service.

A recommendation platform in accordance with a forty-third embodimentincludes the features any of the forty-first and forty-secondembodiments, wherein the retrieved transaction data records storedwithin an immutable ledger include transactions reflecting actionsassociated with user profiles.

A recommendation platform in accordance with a forty-fourth embodimentincludes the features any of the forty-first to forty-third embodiments,wherein the processor is further configured to: determine a precision ofthe recommendation based upon the weighted information from theretrieved data records containing information relevant to the identifiedneed; and transmit the precision to the user device associated with thespecific user profile.

A recommendation platform in accordance with a forty-fifth embodimentincludes the features of the forty-fourth embodiment, wherein theprocessor is further configured to determine that the precision isinsufficient to generate a recommendation.

A recommendation platform in accordance with a forty-sixth embodimentincludes the features of the forty-fifth embodiment, wherein thedetermination that the precision is insufficient to generate arecommendation includes comparing the determined precision to athreshold.

A recommendation platform in accordance with a forty-seventh embodimentincludes the features any of the forty-first to forty-sixth embodiments,wherein the recommendation includes rendering information that directsthe user device how to render the recommendation via a user interface.

A recommendation platform in accordance with a forty-eight embodimentincludes the features any of the forty-first to forty-seventhembodiments, wherein the recommendation includes a plurality ofalternative recommendations.

A recommendation platform in accordance with a forty-ninth embodimentincludes the features any of the forty-first to forty-ninth embodiments,wherein the processor is further configured to determine weights bygenerating a matrix of weights for the specific user profile.

A recommendation platform in accordance with a fiftieth embodimentincludes the features of the forty-ninth embodiment, wherein the matrixof weights include:

-   -   a first dimension that corresponds to different user profiles;        and    -   a second dimension that corresponds to a weighted array, where        each entry in the weighted array corresponds to a weight for one        resource characterized in that the weight indicates the        similarity of the specific user profile with one of the        different user profiles.

A recommendation platform in accordance with a fifty-first embodimentincludes the features of the fiftieth embodiment, wherein the matrix ofweights further includes a third dimension corresponding to differentresources.

A recommendation platform in accordance with a fifty-second embodimentincludes the features any of the fiftieth and fifty-first embodiments,wherein the matrix of weights further includes at least one dimensionrepresenting at least one of temporal aspects, and preferences.

A recommendation platform in accordance with a fifty-third embodimentincludes the features any of the forty-first and fifty-secondembodiments, wherein the processor is further configured to estimate thescarcity of a resource to which the recommendation relates.

A recommendation platform in accordance with a fifty-third embodimentincludes the features any of the forty-first and fifty-secondembodiments,

A recommendation platform in accordance with a fifty-fourth embodimentincludes the features any of the forty-first and fifty-thirdembodiments, wherein the processor is further configured to receive thespecific user profile as an input.

A recommendation platform in accordance with a fifty-fifth embodimentincludes the features of the fifty-fourth embodiment, wherein thespecific user profile includes at least one selected from the groupconsisting of:

-   -   demographic data;    -   action data; and    -   at least one recommendation associated with the specific user        profile.

A recommendation platform in accordance with a fifty-sixth embodimentincludes the features any of the forty-first and fifty-fifthembodiments, wherein the processor is further configured to identifyopinions associated with the specific user profile.

A recommendation platform in accordance with a fifty-seventh embodimentincludes the features any of the forty-first and fifty-sixthembodiments, wherein at least one of the opinions associated with thespecific user profile is identified within the retrieved transactiondata records.

A recommendation platform in accordance with a fifty-eighth embodimentincludes the features any of the forty-first and fifty-seventhembodiments, wherein the processor is further configured to use thespecific user profile to retrieve at least one additional source ofinformation selected from the group consisting of:

-   -   demographic data;    -   action data; and    -   at least one recommendation associated with the specific user        profile.

A recommendation platform in accordance with a fifty-ninth embodimentincludes the features any of the forty-first and fifty-eighthembodiments, wherein the processor is further configured to storeinformation concerning features of the resource as an N-dimensionalfeature vector.

A recommendation platform in accordance with a sixtieth embodimentincludes the features any of the forty-first and fifty-ninthembodiments, wherein the processor is further configured to generate aself-organized map of a plurality of resources based upon N-dimensionalfeature vectors for each of the plurality of resources.

A recommendation platform in accordance with a sixty-first embodimentincludes the features any of the forty-first and sixtieth embodiments,wherein the processor is further configured to generate a lowerdimensional representation of a plurality of resources based uponN-dimensional feature vectors for each of the plurality of resources.

A recommendation platform in accordance with a sixty-second embodimentincludes the features any of the forty-first and sixty-first embodiment,wherein the processor is further configured to identify a subspace of aplurality of resources based upon N-dimensional feature vectors for eachof the plurality of resources.

A recommendation platform in accordance with a sixty-third embodimentincludes the features of the sixty-second embodiment, wherein thesubspace corresponds to a plurality of resources having at least onecharacteristic relevant to the identified need.

A recommendation platform in accordance with a sixty-fourth embodimentincludes the features any of the forty-first and sixty-thirdembodiments, wherein the processor is further configured to detect userprofiles associated with abuse based upon the retrieve transaction datarecords and a representation of a plurality of resources.

A recommendation platform in accordance with a sixty-fifth embodimentincludes the features any of the forty-first and sixty-fourthembodiments, wherein the processor is further configured toautomatically generate user profiles.

A recommendation platform in accordance with a sixty-sixth embodimentincludes the features any of the forty-first and sixty-fifthembodiments, wherein the retrieved transaction data records includeinformation concerning conversion of a previously generatedrecommendation and the processor is further configured to determine anattribution for the conversion to one of the user profiles associatedwith the retrieved data records.

While the above description contains many specific embodiments of theinvention, these should not be construed as limitations on the scope ofthe invention, but rather as an example of one embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiments illustrated, but by the appended claims and theirequivalents.

What is claimed is:
 1. A user device configured to initiate transactionsvia an immutable ledger, comprising: a network interface; memory; and aprocessor, the processor configured to implement an executionenvironment that enables: initiation of transactions via an immutableledger; recordation of events; updating a user profile, where the userprofile comprises at least one characterization associated with the userprofile; encrypting the updated user profile and securely storing theencrypted user profile; receiving a request to access the encrypted userprofile from a process; determining access permissions of the process;and when the process has sufficient access permissions, decrypting theuser profile and providing user profile data to the process.
 2. The userdevice of claim 1, wherein the events comprise user requests and useractions.
 3. The user device of claim 1, wherein the processor is furtherconfigured to transmit user profile data for remote storage.
 4. The userdevice of claim 1, wherein the processor is further configured toreceive user profile data from a remote source and the executionenvironment enables updating of the user profile data of the encrypteduser profile based upon the received user profile data.
 5. The userdevice of claim 1, wherein the processor is further configured to createan append-only area of user profile data using a public key associatedwith the execution environment and a corresponding private key.
 6. Theuser device of claim 1, wherein the request includes a pseudonym and theprocess is determined to have sufficient access permissions when thepseudonym is matches a pseudonym associated with the user profile.
 7. Amethod of restricting access to encrypted user profiles, comprising:updating a user profile based upon events recorded in an immutableledger, where the user profile comprises at least one characterizationassociated with the user profile; encrypting the updated user profileand securely storing the encrypted user profile; receiving a request toaccess the encrypted user profile from a process; determining accesspermissions of the process; and when the process has sufficient accesspermissions, decrypting the user profile and providing user profile datato the process.
 8. The method of claim 7, wherein the events compriseuser requests and user actions.
 9. The method of claim 7, furthercomprising transmitting user profile data for remote storage.
 10. Themethod of claim 7, further comprising receiving user profile data from aremote source and the execution environment enables updating of the userprofile data of the encrypted user profile based upon the received userprofile data.
 11. The method of claim 7, further comprising creating anappend-only area of user profile data using a public key associated withthe execution environment and a corresponding private key.
 12. Themethod of claim 7, wherein the request includes a pseudonym and theprocess is determined to have sufficient access permissions when thepseudonym is matches a pseudonym associated with the user profile.
 13. Anon-transitory computer readable medium containing computer readableinstructions, where execution of the computer readable instructionscauses one or more processors to: update a user profile based uponevents recorded in an immutable ledger, where the user profile comprisesat least one characterization associated with the user profile; encryptthe updated user profile and securely storing the encrypted userprofile; receive a request to access the encrypted user profile from aprocess; determine access permissions of the process; and when theprocess has sufficient access permissions, decrypt the user profile andproviding user profile data to the process.
 14. The non-transitorycomputer readable medium of claim 13, wherein the events comprise userrequests and user actions.
 15. The non-transitory computer readablemedium of claim 13, where execution of the computer readableinstructions further causes one or more processors to transmit userprofile data for remote storage.
 16. The non-transitory computerreadable medium of claim 13, where execution of the computer readableinstructions causes one or more processors to receive user profile datafrom a remote source and the execution environment enables updating ofthe user profile data of the encrypted user profile based upon thereceived user profile data.
 17. The non-transitory computer readablemedium of claim 13, where execution of the computer readableinstructions causes one or more processors to create an append-only areaof user profile data using a public key associated with the executionenvironment and a corresponding private key.
 18. The non-transitorycomputer readable medium of claim 13, wherein the request includes apseudonym and the process is determined to have sufficient accesspermissions when the pseudonym is matches a pseudonym associated withthe user profile.